Pairwise wavelet correlations of single-trial brain waves for Lav in layer III of the aPC, their ranking, and various sets of standard brain waves.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"5500",leadTitle:null,fullTitle:"Genetic Diversity",title:"Genetic Diversity",subtitle:null,reviewType:"peer-reviewed",abstract:"Genetic diversity is the entire amount of genes and genotypes in a group of organisms and is of vital importance for their adaptation to different living conditions. If, for example, all humans were identical, the extinction of the entire kind could happen very fast. Let us care and nourish differences! The goal of this book is to present some of the contemporary thoughts on understandings of the genetic diversity patterns and their altering in a changing world. The book is aimed to the ones inspired to study and contemplate genetic diversity and to the audience beyond any frames.",isbn:"978-953-51-2950-9",printIsbn:"978-953-51-2949-3",pdfIsbn:"978-953-51-5470-9",doi:"10.5772/63174",price:119,priceEur:129,priceUsd:155,slug:"genetic-diversity",numberOfPages:150,isOpenForSubmission:!1,isInWos:1,hash:"ce1bd13553d444bb950f6c4462f98584",bookSignature:"Lidija Bitz",publishedDate:"March 1st 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5500.jpg",numberOfDownloads:6956,numberOfWosCitations:5,numberOfCrossrefCitations:10,numberOfDimensionsCitations:20,hasAltmetrics:0,numberOfTotalCitations:35,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 2nd 2016",dateEndSecondStepPublish:"May 23rd 2016",dateEndThirdStepPublish:"August 27th 2016",dateEndFourthStepPublish:"November 25th 2016",dateEndFifthStepPublish:"December 25th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"153375",title:"Dr.",name:"Lidija",middleName:null,surname:"Bitz",slug:"lidija-bitz",fullName:"Lidija Bitz",profilePictureURL:"https://mts.intechopen.com/storage/users/153375/images/3846_n.jpg",biography:"Lidija Bitz is a principle research scientist of plant genomics at the Natural Resources Institute Finland (Luke). She has a decade of working experience and research exchange from Bosnia and Herzegovina, Denmark, Germany, Netherlands, Sweden and Switzerland. Lidija obtained a MSc degree and defended a PhD thesis at the University of Ljubljana, Slovenia. During those times she was very active in inventory, collection and genetic diversity evaluations within different germplasm. She is active in the dissemination of achieved results through the authorship and editing of monographs, scientific papers, book chapters and professional articles. She has also been successful in implementing international and regional scientific and developmental projects, starting from her student days.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Natural Resources Institute Finland",institutionURL:null,country:{name:"Finland"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"419",title:"Microbial Genetics",slug:"biochemistry-genetics-and-molecular-biology-microbiology-microbial-genetics"}],chapters:[{id:"53510",title:"Diversity of Plant Virus Populations: A Valuable Tool for Epidemiological Studies",doi:"10.5772/66820",slug:"diversity-of-plant-virus-populations-a-valuable-tool-for-epidemiological-studies",totalDownloads:1436,totalCrossrefCites:1,totalDimensionsCites:2,signatures:"Fernando Escriu",downloadPdfUrl:"/chapter/pdf-download/53510",previewPdfUrl:"/chapter/pdf-preview/53510",authors:[{id:"191603",title:"Dr.",name:"Fernando",surname:"Escriu",slug:"fernando-escriu",fullName:"Fernando Escriu"}],corrections:null},{id:"53974",title:"Local Scale Genetic Diversity and its Role in Coping with Changing Climate",doi:"10.5772/67166",slug:"local-scale-genetic-diversity-and-its-role-in-coping-with-changing-climate",totalDownloads:1097,totalCrossrefCites:6,totalDimensionsCites:7,signatures:"Andrés J. Cortés",downloadPdfUrl:"/chapter/pdf-download/53974",previewPdfUrl:"/chapter/pdf-preview/53974",authors:[{id:"190729",title:"Dr.",name:"Andrés",surname:"Cortés",slug:"andres-cortes",fullName:"Andrés Cortés"}],corrections:null},{id:"53953",title:"Genetic Diversity within Chemokine Receptor 5 (CCR5) for Better Understanding of AIDS",doi:"10.5772/67256",slug:"genetic-diversity-within-chemokine-receptor-5-ccr5-for-better-understanding-of-aids",totalDownloads:954,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Ali A. Al-Jabri and Sidgi S. Hasson",downloadPdfUrl:"/chapter/pdf-download/53953",previewPdfUrl:"/chapter/pdf-preview/53953",authors:[{id:"34571",title:"Prof.",name:"Ali",surname:"Al-Jabri",slug:"ali-al-jabri",fullName:"Ali Al-Jabri"}],corrections:null},{id:"53724",title:"From the Gene Sequence to the Phylogeography through the Population Structure: The Cases of Yersinia ruckeri and Vibrio tapetis",doi:"10.5772/67182",slug:"from-the-gene-sequence-to-the-phylogeography-through-the-population-structure-the-cases-of-yersinia-",totalDownloads:664,totalCrossrefCites:0,totalDimensionsCites:1,signatures:"Asmine Bastardo, Sabela Balboa and Jesús L. Romalde",downloadPdfUrl:"/chapter/pdf-download/53724",previewPdfUrl:"/chapter/pdf-preview/53724",authors:[{id:"192422",title:"Dr.",name:"Jesus",surname:"Romalde",slug:"jesus-romalde",fullName:"Jesus Romalde"},{id:"194373",title:"Dr.",name:"Asmine",surname:"Bastardo",slug:"asmine-bastardo",fullName:"Asmine Bastardo"},{id:"194374",title:"Dr.",name:"Sabela",surname:"Balboa",slug:"sabela-balboa",fullName:"Sabela Balboa"}],corrections:null},{id:"53527",title:"Biodiversity Studies in Key Species from the African Mopane and Miombo Woodlands",doi:"10.5772/66845",slug:"biodiversity-studies-in-key-species-from-the-african-mopane-and-miombo-woodlands",totalDownloads:1624,totalCrossrefCites:3,totalDimensionsCites:8,signatures:"Isabel Moura, Ivete Maquia, Alfan A. Rija, Natasha Ribeiro and\nAna Isabel Ribeiro-Barros",downloadPdfUrl:"/chapter/pdf-download/53527",previewPdfUrl:"/chapter/pdf-preview/53527",authors:[{id:"171036",title:"Dr.",name:"Ana",surname:"Ribeiro De Barros",slug:"ana-ribeiro-de-barros",fullName:"Ana Ribeiro De Barros"}],corrections:null},{id:"53443",title:"National and International Conservation of Biological Diversity in Terms of Administrative Law “Sample of Turkey”",doi:"10.5772/66846",slug:"national-and-international-conservation-of-biological-diversity-in-terms-of-administrative-law-sampl",totalDownloads:1181,totalCrossrefCites:0,totalDimensionsCites:2,signatures:"Yavuz Guloglu",downloadPdfUrl:"/chapter/pdf-download/53443",previewPdfUrl:"/chapter/pdf-preview/53443",authors:[{id:"184806",title:"Dr.",name:"Yavuz",surname:"Guloglu",slug:"yavuz-guloglu",fullName:"Yavuz Guloglu"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},relatedBooks:[{type:"book",id:"1446",title:"Senescence",subtitle:null,isOpenForSubmission:!1,hash:"7aa2772cf0b5653b6c599dba90f4c709",slug:"senescence",bookSignature:"Tetsuji Nagata",coverURL:"https://cdn.intechopen.com/books/images_new/1446.jpg",editedByType:"Edited by",editors:[{id:"93967",title:"Dr.",name:"Tetsuji",surname:"Nagata",slug:"tetsuji-nagata",fullName:"Tetsuji Nagata"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1406",title:"Antimicrobial Agents",subtitle:null,isOpenForSubmission:!1,hash:"716194563847e4c8e0f4a7c07ff858ed",slug:"antimicrobial-agents",bookSignature:"Varaprasad Bobbarala",coverURL:"https://cdn.intechopen.com/books/images_new/1406.jpg",editedByType:"Edited by",editors:[{id:"90574",title:"Dr.",name:"Varaprasad",surname:"Bobbarala",slug:"varaprasad-bobbarala",fullName:"Varaprasad Bobbarala"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3509",title:"Gene Therapy",subtitle:"Tools and Potential Applications",isOpenForSubmission:!1,hash:"0fd8b4898c201b4a9f8e597cbcf4d968",slug:"gene-therapy-tools-and-potential-applications",bookSignature:"Francisco Martin Molina",coverURL:"https://cdn.intechopen.com/books/images_new/3509.jpg",editedByType:"Edited by",editors:[{id:"32294",title:"Dr.",name:"Francisco",surname:"Martin",slug:"francisco-martin",fullName:"Francisco Martin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"364",title:"Gene Duplication",subtitle:null,isOpenForSubmission:!1,hash:"79e1de88c46f703c92c157b80d886221",slug:"gene-duplication",bookSignature:"Felix Friedberg",coverURL:"https://cdn.intechopen.com/books/images_new/364.jpg",editedByType:"Edited by",editors:[{id:"62782",title:"Prof.",name:"Felix",surname:"Friedberg",slug:"felix-friedberg",fullName:"Felix Friedberg"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5090",title:"RNA Interference",subtitle:null,isOpenForSubmission:!1,hash:"9edcfa43c752e926f9e51ecb610e34db",slug:"rna-interference",bookSignature:"Ibrokhim Y. Abdurakhmonov",coverURL:"https://cdn.intechopen.com/books/images_new/5090.jpg",editedByType:"Edited by",editors:[{id:"213344",title:"Dr.",name:"Ibrokhim Y.",surname:"Abdurakhmonov",slug:"ibrokhim-y.-abdurakhmonov",fullName:"Ibrokhim Y. Abdurakhmonov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3429",title:"Senescence and Senescence-Related Disorders",subtitle:null,isOpenForSubmission:!1,hash:"2dc962eff773b82b389299073279b4c8",slug:"senescence-and-senescence-related-disorders",bookSignature:"Zhiwei Wang and Hiroyuki Inuzuka",coverURL:"https://cdn.intechopen.com/books/images_new/3429.jpg",editedByType:"Edited by",editors:[{id:"164282",title:"Dr.",name:"Wang",surname:"Zhiwei",slug:"wang-zhiwei",fullName:"Wang Zhiwei"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4558",title:"Advances in DNA Repair",subtitle:null,isOpenForSubmission:!1,hash:"768283d24cc5f9e965ce14d737aa0313",slug:"advances-in-dna-repair",bookSignature:"Clark C. Chen",coverURL:"https://cdn.intechopen.com/books/images_new/4558.jpg",editedByType:"Edited by",editors:[{id:"62462",title:"Prof.",name:"Clark",surname:"Chen",slug:"clark-chen",fullName:"Clark Chen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3428",title:"Meiosis",subtitle:null,isOpenForSubmission:!1,hash:"5be852a0afc01de31a5dd7164bcd025e",slug:"meiosis",bookSignature:"Carol Bernstein and Harris Bernstein",coverURL:"https://cdn.intechopen.com/books/images_new/3428.jpg",editedByType:"Edited by",editors:[{id:"61946",title:"Dr.",name:"Carol",surname:"Bernstein",slug:"carol-bernstein",fullName:"Carol Bernstein"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5944",title:"Applications of RNA-Seq and Omics Strategies",subtitle:"From Microorganisms to Human Health",isOpenForSubmission:!1,hash:"3be741447e351b9cb9dc96a133302c6b",slug:"applications-of-rna-seq-and-omics-strategies-from-microorganisms-to-human-health",bookSignature:"Fabio A. Marchi, Priscila D.R. Cirillo and Elvis C. Mateo",coverURL:"https://cdn.intechopen.com/books/images_new/5944.jpg",editedByType:"Edited by",editors:[{id:"206664",title:"Dr.",name:"Fabio",surname:"Marchi",slug:"fabio-marchi",fullName:"Fabio Marchi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5354",title:"Microsatellite Markers",subtitle:null,isOpenForSubmission:!1,hash:"a53f044725f885fbb6a4f36bde2c9d65",slug:"microsatellite-markers",bookSignature:"Ibrokhim Y. Abdurakhmonov",coverURL:"https://cdn.intechopen.com/books/images_new/5354.jpg",editedByType:"Edited by",editors:[{id:"213344",title:"Dr.",name:"Ibrokhim Y.",surname:"Abdurakhmonov",slug:"ibrokhim-y.-abdurakhmonov",fullName:"Ibrokhim Y. Abdurakhmonov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"74251",slug:"corrigendum-to-enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and",title:"Corrigendum to: Enhancing Soil Properties and Maize Yield through Organic and Inorganic Nitrogen and Diazotrophic Bacteria",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/74251.pdf",downloadPdfUrl:"/chapter/pdf-download/74251",previewPdfUrl:"/chapter/pdf-preview/74251",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/74251",risUrl:"/chapter/ris/74251",chapter:{id:"71840",slug:"enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and-diazotrophic-ba",signatures:"Arshad Jalal, Kamran Azeem, Marcelo Carvalho Minhoto Teixeira Filho and Aeysha Khan",dateSubmitted:"May 29th 2019",dateReviewed:"March 6th 2020",datePrePublished:"April 20th 2020",datePublished:"June 17th 2020",book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"190597",title:"Dr.",name:"Marcelo Carvalho Minhoto",middleName:null,surname:"Teixeira Filho",fullName:"Marcelo Carvalho Minhoto Teixeira Filho",slug:"marcelo-carvalho-minhoto-teixeira-filho",email:"mcm.teixeira-filho@unesp.br",position:null,institution:{name:"Sao Paulo State University",institutionURL:null,country:{name:"Brazil"}}},{id:"322298",title:"Dr.",name:"Aeysha",middleName:null,surname:"Khan",fullName:"Aeysha Khan",slug:"aeysha-khan",email:"fhw9uhfig@gmail.com",position:null,institution:null},{id:"322299",title:"Dr.",name:"Kamran",middleName:null,surname:"Azeem",fullName:"Kamran Azeem",slug:"kamran-azeem",email:"gisfgiog34sg@gmail.com",position:null,institution:null},{id:"322301",title:"Dr.",name:"Arshad",middleName:null,surname:"Jalal",fullName:"Arshad Jalal",slug:"arshad-jalal",email:"gisfgiog3465sg@gmail.com",position:null,institution:null}]}},chapter:{id:"71840",slug:"enhancing-soil-properties-and-maize-yield-through-organic-and-inorganic-nitrogen-and-diazotrophic-ba",signatures:"Arshad Jalal, Kamran Azeem, Marcelo Carvalho Minhoto Teixeira Filho and Aeysha Khan",dateSubmitted:"May 29th 2019",dateReviewed:"March 6th 2020",datePrePublished:"April 20th 2020",datePublished:"June 17th 2020",book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"190597",title:"Dr.",name:"Marcelo Carvalho Minhoto",middleName:null,surname:"Teixeira Filho",fullName:"Marcelo Carvalho Minhoto Teixeira Filho",slug:"marcelo-carvalho-minhoto-teixeira-filho",email:"mcm.teixeira-filho@unesp.br",position:null,institution:{name:"Sao Paulo State University",institutionURL:null,country:{name:"Brazil"}}},{id:"322298",title:"Dr.",name:"Aeysha",middleName:null,surname:"Khan",fullName:"Aeysha Khan",slug:"aeysha-khan",email:"fhw9uhfig@gmail.com",position:null,institution:null},{id:"322299",title:"Dr.",name:"Kamran",middleName:null,surname:"Azeem",fullName:"Kamran Azeem",slug:"kamran-azeem",email:"gisfgiog34sg@gmail.com",position:null,institution:null},{id:"322301",title:"Dr.",name:"Arshad",middleName:null,surname:"Jalal",fullName:"Arshad Jalal",slug:"arshad-jalal",email:"gisfgiog3465sg@gmail.com",position:null,institution:null}]},book:{id:"9345",title:"Sustainable Crop Production",subtitle:null,fullTitle:"Sustainable Crop Production",slug:"sustainable-crop-production",publishedDate:"June 17th 2020",bookSignature:"Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira",coverURL:"https://cdn.intechopen.com/books/images_new/9345.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"76477",title:"Dr.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"8074",leadTitle:null,title:"Lyme Disease",subtitle:null,reviewType:"peer-reviewed",abstract:"This book will be a self-contained collection of scholarly papers targeting an audience of practicing researchers, academics, PhD students and other scientists. The contents of the book will be written by multiple authors and edited by experts in the field.",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"900d78336110fda9e7f46f84187235ff",bookSignature:"",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8074.jpg",keywords:null,numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 24th 2018",dateEndSecondStepPublish:"August 29th 2018",dateEndThirdStepPublish:"October 28th 2018",dateEndFourthStepPublish:"January 16th 2019",dateEndFifthStepPublish:"March 17th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:1,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:null},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"René Mauricio",surname:"Barría",slug:"rene-mauricio-barria",fullName:"René Mauricio Barría"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59676",title:"Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded or Decoded in Single-Trial Oscillatory Brain Waves",doi:"10.5772/intechopen.74810",slug:"wavelet-correlation-analysis-for-quantifying-similarities-and-real-time-estimates-of-information-enc",body:'In the sensory system, a stimulant likely activates stimulant-specific subsets of neurons with a stimulant-specific response profile through the sensory pathway from the sensory organ to the primary sensory cortex, resulting in identical sensory perception of the stimulant. At different stages of this neuronal information processing, the redundancy in sensory information changes by summing or subtracting overlapping signals from cognate and noncognate receptors for common and unique elements. The sensory systems generate oscillatory activities between related cortical regions and the thalamus, except in the olfactory system. The olfactory system generates oscillatory activities in the first and second olfactory centers, the olfactory bulb, and the anterior piriform cortex (aPC). It is significantly more difficult to quantify the degree of similarity or difference in these transient oscillatory responses compared to stationary oscillatory activities. We previously developed a wavelet correlation analysis that is phase-tolerant for transient oscillatory responses and demonstrated a stimulus dependency of the odor-evoked oscillatory brain waves (oscillatory local field potentials, osci-LFPs) in the aPC output layer and an experience dependency in the input layer [1]. These results suggest that the redundancy in the neural representation of olfactory information may change in the aPC.
Sensory systems are incorporated in higher brain functions that synergistically control animal behaviors through multiple neural systems including sensory, memory, decision, motor, or other systems. Generally, all neural systems would maintain the reliability of signal processing in identical activities of identical subsets of neurons in identical time courses through neural pathways with acceptable across-trial variability. This suggests that brain waves in identical behaviors could be, to some extent, reproduced in each brain. Small fluctuations, however, sometimes change oscillatory phases across trials, as has been observed in odor-induced oscillatory brain waves [1]. The fine temporal structures of phase-fluctuated oscillatory activities responsible for informational differences are easily lost by averaging several brain waves, even for identical information in each brain. Associations of single-trial brain waves with in-brain information have been rarely studied. Regarding mental states, the most important individual-independent frequencies of electroencephalography (EEG) are 7–12 Hz at the P1 electrode and <5 Hz at Fz for attention, 10–20 Hz at F4 for fatigue, and 4–7 Hz at Fz and 10–20 Hz at Cz for frustration, with even greater variations in frequencies observed across individuals [2]. Alpha-band oscillations (8–13 Hz) exert top-down influences on the early visual processing for attention orienting [3] and are sensitive markers in the auditory memory loading process [4]. As a test case, we applied a wavelet correlation analysis to estimate odor information in the fine temporal structures of single-trial brain waves.
Odor-evoked oscillatory brain waves in the aPC are not stationary over the time window of interest, even in an ex vivo isolated whole brain with attached nose preparation under the condition of no inputs from the nonolfactory sensory systems (Figure 1) [1, 5]. Oscillatory brain waves initiate during the 1-s odor presentation before the peak of the receptor potential, the electro-olfactogram (EOG) (the lowest trace in Figure 1) [1]. A pair of quite different odors, lavender essential oil (Lav), and a mixture of three fatty acids—mc4 + mc6 + mc8 (mc468)—were selected as plant- and animal-related odors, respectively. Linalool (Lina) and n-butanoic acid (mc4) were selected as the single-compound odors of Lav and mc468, respectively, with partial overlaps of the activated olfactory receptors and their respective signal pathways with their original mixtures as well as 0.1 Lav (10-fold diluted Lav). As expected, oscillatory brain waves of a pair of quite different Lav and mc468 odors look dissimilar in the initial phase but are partially similar in the late phase.
Odor-evoked oscillatory brain waves in layer I of the anterior piriform cortex (aPC) [1]. Time courses of low-pass-filtered (0–45 Hz) oscillatory brain waves and the receptor potential (electro-olfactogram, EOG) at the centromedial or caudocentral** site of the aPC in the isolated whole brain are shown for three odors (Lav, lavender essential oil as an odor from a plant; 0.1 Lav (10-fold diluted Lav); and mc468, a mixture of three fatty acids as an imitated odor from animals). Ringer solution (RN) was used as a control. The odor or RN was presented to the nose of the isolated brain for 1 or 4 s* (only for the sixth Lav), as indicated by the horizontal bar in the in-presentation order for each odor (entire presentation order). The responses in the 2.5-s time window* of interest were analyzed.
The correlations of the temporal profiles of oscillatory brain waves in the aPC for a 2.5-s time window, which comprised the 1-s odor presentation and the following 1.5 s, were not homogeneously high between identical odors (Figure 2A) [1]. Only a few identical odor pairs for Lav or 0.1 Lav demonstrated relatively high correlations (0.7–0.74), whereas the remaining pairs demonstrated intermediate (0.47–0.69) or low (0.29) correlations. These low correlations are attributable to the independent fluctuations in the oscillatory phase angles and powers including a few synchronous cycles (indicated by the daggers), in the fast Fourier transform (FFT) components even between identical odors, indicating that oscillatory responses are not strictly phase-locked to the stimulus onset (Figure 3) [1]. The spurious high correlations of the 0–45 Hz components are attributable to the similarities in the temporal profiles of the 0–2 Hz components [1]. The 0–2 Hz component resulted in high correlations (>0.77) for all the Lav and 0.1-Lav pairs (Figure 2B), whereas the 2–45 Hz components resulted in low correlations (<0.4) for all pairs (Figure 2C). To address these weaknesses of the conventional analyses, we tested a novel correlation analysis of wavelet profiles.
Correlation matrices among odor-evoked oscillatory brain waves in layer I of the aPC [1]. (A) Matrix of cross-/autocorrelations of the 0–45 Hz components of the odor-evoked oscillatory brain waves in the 2.5-s time window* of interest (shown in Figure 1). Some of the identical odor pairs produced high correlations >0.7. Identical odors are grouped in the order of stimulus presentation. (B) Cross-/autocorrelation matrix of the 0–2 Hz components of the odor-evoked oscillatory brain waves. (C) Cross/autocorrelation matrix of the 2–45 Hz components of the odor-evoked oscillatory brain waves. By omitting the 0–2 Hz component, all correlations were reduced to <0.4. (D) the matrix in B rearranged in the entire presentation order did not demonstrate an approach of the high correlations of the 0–2 Hz components to the diagonal line (between the dashed lines). The color represents the respective amplitude range of the cross-correlations: black, <0.60; green, 0.60–0.69; pink, 0.70–0.79; red, 0.80–0.89; orange, 0.90–0.99; and white, 1.00.
The oscillatory phases of the odor-evoked oscillatory brain waves differed between identical stimuli [1]. The 0–45 Hz and six frequency band components of the odor-evoked oscillatory brain waves were obtained by using an FFT bandpass filter. The two responses in the left and middle columns were superimposed on the respective frequency bands in the right column, indicating the trial-by-trial oscillatory phase differences and their fluctuations. The phase-matching points are indicated by the daggers.
Figure 4 shows the procedure for the wavelet transformation and its conversion to a data array for the wavelet correlation analysis [1]. The wavelet time-frequency power profiles enable us to quantify the similarity of the odor-evoked oscillatory brain waves. The wavelet transform is like a running, windowed Fourier transform; it uses a certain window size and slides it along in time, computing the FFT at each time using only the data within the window. The original wavelet software libraries were provided by Torrence and Compo [6] and modified with respect to the following points. Because of the spurious high correlations in the low-frequency band, all 0–2 Hz components were removed prior to the phase-tolerant analysis of the 2–45 Hz components of the oscillatory brain waves. The 2–45 Hz bandpass-filtered brain waves (Figure 4A) were subjected to a Morlet wavelet analysis by using the following equations:
Wavelet transformation and wavelet cross-correlation profile of an oscillatory response [1]. (A) The 2–45 Hz component of a single-trial 1-s odor-evoked oscillatory brain wave (oscillatory local field potentials, osci-LFPs) in the anterior piriform cortex in an isolated guinea-pig whole brain (second presentation of lavender odor, indicated by the bold bar). (B) A Morlet wavelet time-frequency power spectrum of the second Lav-evoked oscillatory brain wave. Subsequently, seven sets of 2048-point wavelet transformations of the oscillatory brain waves were computed. (C) A columnar array of wavelet cross-/autocorrelations of the second Lav-evoked response. One of the responses for the 2.5-s time window at nine representative frequencies and sets of logarithmic ratios of the cross-correlation to the autocorrelation between wavelet pairs of the second Lav-evoked response (target) were serially concatenated into a data array, in which the wavelet correlations were calculated as correlation coefficients.
where (*) indicates the complex conjugate, ω0 = 6, N = 2048, δt = 0.001, s0 = 2δt, and δj = 0.1. The wavelet power spectrum,
The wavelet profiles of odor-evoked oscillatory brain waves differed between the input and output layers of the aPC [1]. Of the 21 pairs of 1-s odor-evoked oscillatory brain waves (upper traces) that were simultaneously recorded in layers I (input) or III (output) of the aPC, 10 pairs are represented. In the wavelet time-frequency power profiles (lower traces) for the 2.2-s time window (marked by the asterisk), the ~10 Hz components remained prominent in layer III, whereas the <8 Hz components became less prominent compared to those in layer I. The in-stimulant presentation order is indicated. Statistically significant oscillatory powers were located within the black lines compared to those before presentation of odors (P < 0.0001, chi-squared test).
We calculated correlation coefficients between logarithmic ratio arrays of the cross-correlations to the autocorrelations of the wavelet power profile for the time window of interest at the following nine representative frequencies (selected from the calculated wavelet frequencies) to quantify the similarities of the wavelet time-frequency power profiles between identical and different odors:
Delta (2–4 Hz): 3.78 Hz.
Theta (4–8 Hz): 7.56 Hz.
Alpha (8–13 Hz): 10.7 Hz for the dominant oscillation and 12.29 Hz.
Low beta (13–20 Hz): 15.13 Hz.
High beta (20–30 Hz): 21.39 and 26.33 Hz.
Gamma (30–45 Hz): 30.25 and 34.75 Hz.
The cross-correlation was calculated as the sum of the products of the wavelet power for a pair comprising the target response (
A serially concatenated columnar array of all sets of the nine logarithmic ratios of the cross-correlations to the autocorrelations of the target response in the identical order of responses is a form of a wavelet cross-correlation profile (Figure 4C) [1]. The wavelet correlations were calculated as the correlation coefficients between these columnar arrays and employed to quantify the similarities of the odor-evoked oscillatory brain waves in the aPC.
Other mother wavelets such as Meyer and Mexican hat were considered to be inadequate for application to the odor-evoked oscillatory brain waves because their shapes appeared more dissimilar to any FFT components of the oscillatory brain waves than that of the Morlet (Figure 3). To date, except for one case [1], there are no published results of quantifying the similarities between oscillatory brain waves. Regarding the time-frequency power profiles, three reports were found. In one study, a discrete wavelet transform was used to identify and compare the timings of spike trains in an insect antennal lobe (corresponding to the mammal olfactory bulb) [7]. In another study, the Morlet wavelet transform was used to identify dominant oscillatory frequency bands and the synchrony between the oscillatory brain waves in different olfactory regions [8]. In the third study, the Hilbert transform was used to identify the dominant oscillations of the odor-evoked responses in the theta band in the posterior piriform cortex with phase-locked activities in the hippocampus in humans [9]. The Hilbert transform produced similar oscillation powers in a wide frequency range of 60–140 Hz, which is inconsistent with the decreased powers of the Morlet wavelet. Considering these results, we did not intend to analyze the odor-evoked oscillatory brain waves with the Meyer or Mexican hat mother wavelets or the Hilbert transform.
The wavelet correlation analysis revealed that the olfactory information redundancy of a neural representation changes from experience (high redundancy) to a stimulus dependency (low redundancy) in the aPC [1]. The origins of the activities in layer I of the aPC are mainly the afferent fibers (input), association fibers, and postsynaptic inhibitory feedback input, whereas the activities in layer III primarily originate from the responses (output) of pyramidal cells, which are the principal neurons in the aPC and receive signals from multiple ORs. The wavelet profiles of identical odors resembled each other more than they resembled those of different odors in layers I (input signals) and III (output signals) of the aPC (Figure 5) [1]. In addition, the wavelet transformation visualized moderately clustered spot-like transient reductions in oscillatory power at frequencies just above 10 Hz in the odor-evoked oscillatory brain waves in layer I of the aPC (Figure 5). The most characteristic odor-dependent differences appeared in the initial phase of the wavelets for odor-evoked oscillatory brain waves in layer I of aPC. The mc468-evoked oscillatory brain wave was markedly greater especially at low frequencies in the initial phase than that of the Lav-evoked response [1].
The array data of the logarithmic ratios of the wavelet cross-/autocorrelations between 21 odor-evoked oscillatory brain waves differed slightly between layers I and III of the aPC (Figure 6) [1]. The lengths of the bars reflect the differences between a pair of oscillatory brain waves in such a way that the values of +1, 0, and −1 represent cross-correlations that are 10-fold, equal to, and one-tenth of the autocorrelation at the respective frequencies.
The wavelet cross-correlation profiles of odor-evoked oscillatory brain waves slightly differed between the input and output layers of the aPC [1]. The five pairs of logarithmic ratio arrays of the wavelet cross-/autocorrelations are exemplified. These ratio arrays suggest that the mc468-evoked responses markedly differed from those of Lav or Lina in each layer of the aPC and that they slightly differed between the input and output layers.
In layer III, the Lav odor pairs (broken yellow square in Figure 7C) showed homogeneously high correlations, except for the ninth Lav, whereas the identical Lav pairs in layer I resulted in more heterogeneous correlations (Figure 7A) [1]. In addition, the correlations between different single-component odors (Lina and mc4, in the broken blue squares in Figure 7C) decreased to <0.6 in layer III, whereas the corresponding correlations in layer I were mostly greater than 0.6 (Figure 7A) [1]. Notably, the heterogeneous correlations changed into an experience-dependent response similarity, which was observed for some of the odors in layer I of the aPC (a cluster of high correlations between the dashed lines in Figure 7B vs. 7A) but was not clearly observed in layer III (Figure 7D vs. 7A) as well as the 0–2 Hz components in layer I (Figure 2D) [1]. In layer III, the <8 Hz components decreased relative to those in layer I, with the prominent ~10 Hz oscillation remaining [1]. These results indicate a change in the neuronal information redundancy of transient and oscillatory brain waves from the dependencies on stimulus experience (high redundancy) to stimulus quality (low redundancy) between the input and output layers of the aPC. Recently, in the olfactory bulb that is upstream of the aPC in the olfactory pathway, stimulus history-dependent odor processing was observed [10]. This means that the wavelet correlation analysis had revealed a consistent experience dependency in input signals in the aPC from the olfactory bulb.
The wavelet correlation matrices of oscillatory brain waves differed between the input and output signals in the aPC [1]. (A) The wavelet correlation matrix of oscillatory brain waves in layer I (input) of the aPC. (B) The matrix in A rearranged in the entire presentation order. High correlations approached the diagonal line. (C) The wavelet correlation matrix of osci-LFPs in layer III (output) of the aPC. (D) The matrix in C rearranged in the entire presentation order. The colors representing power magnitudes are the same as in Figure 2.
We evaluated the ability of the wavelet correlation analysis to detect changes in oscillatory powers at specific frequencies by 0.2-fold step modified wavelet powers at 1–8 frequency bands (Figure 8) [1]. Greater decreases in correlations (0.4–0.7) were observed as a result of the 0.2-fold power modification at only 1–2 frequencies than those of eight frequencies (number/9 given in parentheses on the Y-axis). For 0.2-fold power amplification, the largest and smallest decreases were observed at 8–13 and 4–8 Hz, respectively. This analysis revealed that in the aPC, the 8–13 Hz component of the oscillatory brain waves contributes to the correlation coefficients more than the 4–8 Hz component. The wavelet correlation analysis enables the estimation of the relative contributions of oscillatory components to the similarities and differences between oscillatory brain waves.
Sensitivity of the wavelet correlation analysis to changes in the oscillatory components [1]. A 0.2-fold power amplification resulted in the largest and smallest decreases in the wavelet correlations for 8–13 and 4–8 Hz, respectively. As the number of power-modified frequencies increased to more than four, changes in the wavelet correlations were reduced.
Here, the odor-evoked brain waves were the same as those used in the previous section. To estimate the in-brain information, two standard brain waves, covering a wide range of variations for identical information, were selected. The criteria for selecting the two standard brain waves were as follows: (i) a brain wave with the highest pairwise correlation coefficient and a high average of pairwise correlation coefficients in the given information for each individual and (ii) a brain wave with the second highest pairwise correlation coefficient and a differently ranked average of pairwise correlation coefficients in the given information for the same individual.
To select standard brain waves for the four odors, the correlation coefficients in the 2.2-s time window of interest were ranked between single-trial brain waves for all possible pairs of identical odors. Among the 28 pairs of brain waves for Lav, the highest correlation was obtained for the second Lav and fourth Lav pair that provided the fourth (median) and second highest averages of pairwise correlation coefficients, respectively (Table 1). The second highest correlation coefficient was obtained for the third and fifth Lav brain wave pair that provided the seventh and third highest averages of pairwise correlation coefficients, respectively. On the basis of the criteria, the fourth and third Lav brain waves were selected as the two standard brain waves for Lav information.
Ranking of wavelet correlations | Standard set | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Lav | First Lav | Second Lav | Third Lav | Fourth Lav | Sixth Lav | Seventh Lav | Eighth Lav | Ninth Lav | Corr. coeff. Rank | Ave. corr. Coeff. | Ave. rank | Memo. | 1 | 1-m1 | 1-m1p1 | 1-mp | 2 | 2-m2p | s1 | s1 m1 | s2 |
First Lav | 1.00 | 0.63995 | 0.26 | 0.59 | 0.44 | 0.59 | 0.60 | 0.31 | 6 | 0.55 | 6 | ||||||||||
Second Lav | 0.64 | 1.00 | 0.60 | 0.73 | 0.60 | 0.52 | 0.47 | 0.11 | 1 | 0.59 | 4 | median | ○ | ○ | |||||||
Third Lav | 0.26 | 0.60 | 1.00 | 0.47 | 0.683 | 0.28 | 0.54 | 0.38 | 2 | 0.53 | 7 | △ | ○ | ○ | ○ | ○ | |||||
Fourth Lav | 0.59 | 0.73 | 0.47 | 1.00 | 0.68 | 0.64 | 0.50 | 0.21 | 1 | 0.60 | 2 | ◎ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | |
Sixth Lav | 0.44 | 0.60 | 0.683 | 0.682 | 1.00 | 0.55 | 0.51 | 0.30 | 2 | 0.60 | 3 | ||||||||||
Seventh Lav | 0.59 | 0.52 | 0.28 | 0.63998 | 0.55 | 1.00 | 0.59 | 0.41 | 5 | 0.57 | 5 | ||||||||||
Eighth Lav | 0.60 | 0.47 | 0.54 | 0.50 | 0.51 | 0.59 | 1.00 | 0.66 | 4 | 0.61 | 1 | ○ | |||||||||
Ninth Lav | 0.31 | 0.11 | 0.38 | 0.21 | 0.30 | 0.41 | 0.66 | 1.00 | 4 | 0.42 | 8 |
Pairwise wavelet correlations of single-trial brain waves for Lav in layer III of the aPC, their ranking, and various sets of standard brain waves.
With regard to the pairwise correlation coefficients, their values for Lav pairs tended to be greater than those for mc4 pairs, and the values for Lina pairs tended to be greater than those for mc468 pairs. The lower correlation coefficients between identical odors suggest a greater across-trial variability in the time-frequency power profiles of single-trial brain waves, despite the tolerance of oscillatory phase differences. Similarly, the first and third Lina brain waves (Table 2), the fourth and first mc4 brain waves (Table 3), and the third and first mc468 brain waves (Table 4) were selected as standard brain waves for the respective information. These eight standard brain waves, as well as a control brain wave evoked by an odorless Ringer solution (second RN), were used as Set 1 of standard brain waves.
Ranking of wavelet correlations | Standard set | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Lina | First Lina | Second Lina | Third Lina | Fourth Lina | Corr. coeff. Rank | Ave. corr. Coeff. | Ave. rank | Memo. | 1 | 1-m1 | 1-m1p1 | 1-mp | 2 | 2-m2p | s1 | s1 m1 | s2 |
First Lina | 1.00 | 0.49 | 0.22 | 0.04 | 1 | 0.44 | 1 | ◎ | ○ | ○ | ○ | ○ | ○ | ||||
Second Lina | 0.49 | 1.00 | −0.13 | 0.18 | 1 | 0.38 | 3 | Median | ○ | ○ | ○ | ○ | |||||
Third Lina | 0.22 | −0.13 | 1.00 | 0.34 | 2 | 0.36 | 4 | △ | ○ | ||||||||
fourth Lina | 0.04 | 0.18 | 0.34 | 1.00 | 2 | 0.39 | 2 | ○ | ○ | ○ | ○ | ○ |
Pairwise wavelet correlations of single-trial brain waves for Lina in layer III of the aPC, their ranking, and various sets of standard brain waves.
Ranking of wavelet correlations | Standard set | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mc4 | First mc4 | Second mc4 | Third mc4 | Fourth mc4 | Fifth mc4 | Corr. coeff. Rank | Ave. corr. Coeff. | Ave. rank | Memo. | 1 | 1-m1 | 1-m1p1 | 1-mp | 2 | 2-m2p | s1 | s1 m1 | s2 |
First mc4 | 1.00 | 0.04 | 0.467 | 0.35 | 0.15 | 2 | 0.40 | 5 | △ | ○ | ○ | ○ | ||||||
Second mc4 | 0.04 | 1.00 | 0.25 | 0.366 | 0.368 | 4 | 0.40 | 4 | ||||||||||
Third mc4 | 0.467 | 0.25 | 1.00 | 0.46 | 0.18 | 2 | 0.47 | 2 | ○ | |||||||||
Fourth mc4 | 0.35 | 0.37 | 0.46 | 1.00 | 0.472 | 1 | 0.53 | 1 | ◎ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | ○ |
Fifth mc4 | 0.15 | 0.37 | 0.18 | 0.472 | 1.00 | 1 | 0.43 | 3 | Median | ○ | ○ |
Pairwise wavelet correlations of single-trial brain waves for mc4 in layer III of the aPC, their ranking, and various sets of standard brain waves.
Ranking of wavelet correlations | Standard set | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mc468 | First mc468 | Third mc468 | Fourth mc468 | Corr. coeff. Rank | Ave. corr. Coeff. | Ave. rank | Memo. | 1 | 1-m1 | 1-m1p1 | 1-mp | 2 | 2-m2p | s1 | s1 m1 | s2 |
First mc468 | 1.00 | 0.14 | 0.05 | 2 | 0.39 | 3 | △ | ○ | ○ | |||||||
Third mc468 | 0.14 | 1.00 | 0.23 | 1 | 0.46 | 1 | ◎ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | ○ | ○ |
Fourth mc468 | 0.05 | 0.23 | 1.00 | 1 | 0.43 | 2 | Median | ○ | ○ | ○ | ○ |
Pairwise wavelet correlations of single-trial brain waves for mc468 in layer III of the aPC and various sets of standard brain waves.
Using the wavelet correlation analysis, all possible pairwise correlation coefficients between a given single-trial brain wave and each standard brain wave (Set 1) were calculated. The first candidate was selected as the standard brain wave with the highest correlation coefficient to a target single-trial brain wave. The wavelet correlation analysis provided the first candidates for 12 single-trial brain waves with an accuracy of 75% (Table 5). An accuracy of 100% was achieved for Lina (2/2) and mc468 (1/1), whereas an accuracy of 67% was achieved for Lav (4/6) and mc4 (2/3). Notably, the single-trial brain waves tested were not any of the Set 1 standard brain waves. The accuracy of the first candidates was more than threefold higher than chance in five cases (20%). The probability of including the correct information for the two upper candidates was 92% (Table 5). However, the third candidates did not improve the probability of including the correct information for the three upper candidates (92%). In the estimates of information, candidates with correlation coefficients <0.6 were disregarded as nonspecific ones.
Standard brain waves | Third Lav | Fourth Lav | First Lina | Third Lina | First mc468 | Third mc468 | First mc4 | Fourth mc4 | Second RN | Highest corr. | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Third Lav | 1.00 | 0.67 | 0.85 | 0.66 | 0.63 | 0.50 | 0.44 | 0.36 | 0.45 | Lina | ||
Fourth Lav | 0.67 | 1.00 | 0.58 | 0.61 | 0.70 | 0.54 | 0.28 | 0.54 | 0.41 | mc468 | ||
First Lina | 0.85 | 0.58 | 1.00 | 0.60 | 0.58 | 0.40 | 0.41 | 0.30 | 0.46 | Lav | ||
Third Lina | 0.66 | 0.61 | 0.60 | 1.00 | 0.63 | 0.72 | 0.54 | 0.58 | 0.41 | mc468 | ||
First mc468 | 0.63 | 0.70 | 0.58 | 0.63 | 1.00 | 0.728 | 0.63 | 0.723 | 0.40 | mc468 | ||
Third mc468 | 0.50 | 0.54 | 0.40 | 0.72 | 0.73 | 1.00 | 0.50 | 0.63 | 0.52 | mc468 | ||
First mc4 | 0.44 | 0.28 | 0.41 | 0.54 | 0.63 | 0.50 | 1.00 | 0.73 | 0.35 | mc4 | ||
Fourth mc4 | 0.36 | 0.54 | 0.30 | 0.58 | 0.72 | 0.63 | 0.73 | 1.00 | 0.37 | mc4 | ||
Second RN | 0.45 | 0.41 | 0.46 | 0.41 | 0.40 | 0.52 | 0.35 | 0.37 | 1.00 | — | ||
Single-trial brain waves | Estimated information | Second candidate (>0.6) | Third candidate (>0.6) | |||||||||
First Lav | 0.56 | 0.77 | 0.47 | 0.48 | 0.53 | 0.47 | 0.39 | 0.59 | 0.49 | Lav | — | — |
Second Lav | 0.69 | 0.82 | 0.62 | 0.56 | 0.51 | 0.43 | 0.26 | 0.41 | 0.58 | Lav | Lav | — |
Sixth Lav | 0.774 | 0.78 | 0.766 | 0.79 | 0.69 | 0.51 | 0.40 | 0.46 | 0.39 | Lina | Lav | Lav |
Seventh Lav | 0.53 | 0.79 | 0.42 | 0.65 | 0.71 | 0.63 | 0.50 | 0.75 | 0.51 | Lav | mc4 | mc468 |
Eighth Lav | 0.641 | 0.693 | 0.52 | 0.689 | 0.68 | 0.56 | 0.63 | 0.63 | 0.33 | Lav | Lina | mc468 |
Ninth Lav | 0.51 | 0.43 | 0.46 | 0.61 | 0.73 | 0.51 | 0.74 | 0.72 | 0.20 | mc4 | mc468 | mc4 |
Second Lina | 0.71 | 0.44 | 0.79 | 0.53 | 0.56 | 0.29 | 0.48 | 0.29 | 0.28 | Lina | Lav | — |
Fourth Lina | 0.652 | 0.56 | 0.654 | 0.79 | 0.71 | 0.63 | 0.57 | 0.47 | 0.24 | Lina | mc468 | Lina |
Fourth mc468 | 0.58 | 0.56 | 0.54 | 0.775 | 0.777 | 0.86 | 0.60 | 0.63 | 0.33 | mc468 | mc468 | Lina |
Second mc4 | 0.36 | 0.44 | 0.23 | 0.60 | 0.68 | 0.84 | 0.58 | 0.80 | 0.51 | mc468 | mc4 | mc468 |
Third mc4 | 0.35 | 0.35 | 0.25 | 0.55 | 0.66 | 0.61 | 0.81 | 0.85 | 0.34 | mc4 | mc4 | mc468 |
Fifth mc4 | 0.36 | 0.45 | 0.25 | 0.57 | 0.54 | 0.55 | 0.68 | 0.81 | 0.41 | mc4 | mc4 | — |
Correct rate | 75% | 92% | 92% |
Estimated information of single-trial brain waves in layer III of the aPC by ranking of wavelet correlations using two standard brain waves (set 1).
To compare the ideal set of standard brain waves (Set 1) with different sets of standard brain waves (standard Set 1-m) in terms of their accuracies for estimating information, wavelet correlation analyses were performed with partial replacements of standard brain waves. When one or three of the nine Set 1 standard brain waves were replaced with brain waves that did not meet the criteria, there were no changes in the 75% accuracy for the first candidates, and a 92% probability of including the correct information for the two upper candidates was observed. Nevertheless, there were some exchanges between correct and incorrect estimates for identical information (data not shown).
In contrast, by using the pair of brain waves with the highest pairwise correlation coefficients as the two standard brain waves for each odor (standard Set 2), the accuracies of estimation were reduced by 100% for Lina (2/2 → 0/2) and 34% for Lav (4/6 → 2/6), but no change occurred for mc468 (1/1) and mc4 (2/3) (Table 6). This standard Set 2 provided a total accuracy of 42% (33% reduction) and a 75% probability (17% reduction) of including the correct information for the two upper candidates (Figure 9). By replacing two of the nine Set 2 standard brain waves with one that did not meet the criteria, the accuracy for the first candidates increased by 25% and the 92% probability of including the correct information for the two upper candidates was recovered (Figure 9). Therefore, the proposed criteria of selecting standard brain waves with a wide variation are likely appropriate and achieve better estimation than the selection of those with a narrow range (the most similar brain wave pairs).
Standard brain waves | Second Lav | Fourth Lav | First Lina | Second Lina | Third mc468 | Fourth mc468 | Fourth mc4 | Fifth mc4 | Second RN | Highest corr. | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Second Lav | 1.00 | 0.80 | 0.57 | 0.47 | 0.40 | 0.37 | 0.35 | 0.39 | 0.55 | Lav | ||
Fourth Lav | 0.80 | 1.00 | 0.47 | 0.37 | 0.50 | 0.54 | 0.50 | 0.46 | 0.35 | Lav | ||
First Lina | 0.57 | 0.47 | 1.00 | 0.80 | 0.38 | 0.56 | 0.28 | 0.24 | 0.42 | Lina | ||
Second Lina | 0.47 | 0.37 | 0.80 | 1.00 | 0.28 | 0.49 | 0.29 | 0.29 | 0.27 | Lina | ||
Third mc468 | 0.40 | 0.50 | 0.38 | 0.28 | 1.00 | 0.85 | 0.54 | 0.55 | 0.48 | mc468 | ||
Fourth mc468 | 0.37 | 0.54 | 0.56 | 0.49 | 0.85 | 1.00 | 0.58 | 0.53 | 0.29 | mc468 | ||
Fourth mc4 | 0.35 | 0.50 | 0.28 | 0.29 | 0.54 | 0.58 | 1.00 | 0.83 | 0.27 | mc4 | ||
Fifth mc4 | 0.39 | 0.46 | 0.24 | 0.29 | 0.55 | 0.53 | 0.83 | 1.00 | 0.38 | mc4 | ||
Second RN | 0.55 | 0.35 | 0.42 | 0.27 | 0.48 | 0.29 | 0.27 | 0.38 | 1.00 | — | ||
Single-trial brain waves | Estimated information | Second candidate (>0.6) | Third candidate (>0.6) | |||||||||
First Lav | 0.78 | 0.76 | 0.42 | 0.36 | 0.44 | 0.41 | 0.58 | 0.70 | 0.43 | Lav | Lav | mc4 |
Third Lav | 0.61 | 0.58 | 0.87 | 0.76 | 0.49 | 0.62 | 0.35 | 0.36 | 0.41 | Lina | Lina | mc468 |
Sixth Lav | 0.65 | 0.73 | 0.70 | 0.74 | 0.56 | 0.71 | 0.41 | 0.52 | 0.32 | Lina | Lav | mc468 |
Seventh Lav | 0.62 | 0.74 | 0.36 | 0.40 | 0.57 | 0.63 | 0.72 | 0.65 | 0.41 | Lav | mc4 | mc4 |
Eighth Lav | 0.61 | 0.68 | 0.47 | 0.52 | 0.57 | 0.63 | 0.65 | 0.72 | 0.33 | mc4 | Lav | mc4 |
Ninth Lav | 0.27 | 0.40 | 0.42 | 0.42 | 0.47 | 0.60 | 0.75 | 0.70 | 0.18 | mc4 | mc4 | mc468 |
Third Lina | 0.52 | 0.57 | 0.55 | 0.54 | 0.69 | 0.77 | 0.54 | 0.60 | 0.40 | mc468 | mc468 | mc4 |
Fourth Lina | 0.34 | 0.50 | 0.62 | 0.70 | 0.60 | 0.84 | 0.44 | 0.40 | 0.18 | mc468 | Lina | Lina |
First mc468 | 0.50 | 0.66 | 0.56 | 0.55 | 0.71 | 0.79 | 0.66 | 0.55 | 0.35 | mc468 | mc468 | mc4 |
First mc4 | 0.17 | 0.22 | 0.37 | 0.45 | 0.45 | 0.57 | 0.75 | 0.68 | 0.27 | mc4 | mc4 | — |
Second mc4 | 0.26 | 0.42 | 0.21 | 0.15 | 0.83 | 0.70 | 0.71 | 0.72 | 0.48 | mc468 | mc4 | mc4 |
Third mc4 | 0.19 | 0.32 | 0.24 | 0.28 | 0.60 | 0.63 | 0.85 | 0.77 | 0.28 | mc4 | mc4 | mc468 |
Correct rate | 42% | 75% | 75% |
Estimated information of single-trial brain waves in layer III of the aPC by ranking of wavelet correlations using two standard brain waves with the highest pairwise correlation coefficients (set 2).
Variation-dependent changes in the accuracy of estimated information of single-trial brain waves in layer III of the aPC.
By using a set of single standard brain waves for four odors that met only the first criterion (standard Set s1), a similar accuracy of estimated information and probability of including the correct information for the two upper candidates was obtained for the 12 target brain waves (data not shown). The Set s1 standard brain waves were composed of the fourth Lav, first Lina, third mc468, fourth mc4, and second RN. Among the 16 target brain waves, the accuracy and probability slightly decreased by 6 and 4%, respectively, compared to those of the 12 target brain waves (data not shown). When one or two of the five Set-s1 standard brain waves were replaced with those that did not meet the criteria, the accuracy was reduced to 67 or 42%, respectively (data not shown). The probability of including the correct information for the two upper candidates was also reduced by 9 and 25%, respectively. For the 16 target brain waves, the accuracy and probability showed almost no changes when one of the five Set s1 standard brain waves was replaced, whereas the accuracy and probability for the estimated information were reduced by 13% when two of the Set s1 standard brain waves were replaced (data not shown).
It is interesting to examine the accuracy of the wavelet correlation analysis for predicting the in-brain information of single-trial brain waves comprising redundant signals in layer I of the aPC. By using a set of standard brain waves that meet the proposed criteria for the redundant brain waves recorded in layer I (standard Set 1r), the wavelet correlation analysis provided a similar accuracy (75%) of estimated information and probability (100%) of including the correct information for the two upper candidates (Table 7) compared to the results observed for the brain waves recorded in layer III (Table 5). In contrast, by using the pairs of brain waves corresponding to the Set 1 of layer III (standard Set 1′ in layer I), the accuracy of estimation was reduced by 17%, and the probability of including the correct information for the two upper candidates was reduced by 25% (to 75%) (data not shown). By using single standard brain waves (standard Set s1r), the accuracy and probability were slightly reduced compared to those of the standard Set s1 (data not shown).
Standard brain waves | Second Lav | Eighth Lav | Second Lina | Third Lina | First mc468 | Fourth mc468 | First mc4 | Fifth mc4 | Second RN | Highest corr. | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Second Lav | 1.00 | 0.56 | 0.64 | 0.73 | 0.63 | 0.56 | 0.59 | 0.42 | 0.47 | Lina | ||
Eighth Lav | 0.56 | 1.00 | 0.47 | 0.50 | 0.57 | 0.63 | 0.66 | 0.76 | 0.09 | mc4 | ||
Second Lina | 0.64 | 0.47 | 1.00 | 0.67 | 0.85 | 0.68 | 0.76 | 0.49 | 0.48 | mc468 | ||
Third Lina | 0.73 | 0.50 | 0.67 | 1.00 | 0.69 | 0.75 | 0.74 | 0.48 | 0.25 | mc468 | ||
First mc468 | 0.63 | 0.57 | 0.85 | 0.69 | 1.00 | 0.79 | 0.81 | 0.54 | 0.35 | Lav | ||
Fourth mc468 | 0.56 | 0.63 | 0.68 | 0.75 | 0.79 | 1.00 | 0.87 | 0.58 | 0.23 | mc4 | ||
First mc4 | 0.59 | 0.66 | 0.76 | 0.74 | 0.81 | 0.87 | 1.00 | 0.69 | 0.30 | mc468 | ||
Fifth mc4 | 0.42 | 0.76 | 0.49 | 0.48 | 0.54 | 0.58 | 0.69 | 1.00 | 0.20 | Lav | ||
Second RN | 0.47 | 0.09 | 0.48 | 0.25 | 0.35 | 0.23 | 0.30 | 0.20 | 1.00 | — | ||
Single-trial brain waves | Estimated information | Second candidate (>0.6) | Third candidate (>0.6) | |||||||||
First Lav | 0.77 | 0.70 | 0.66 | 0.52 | 0.59 | 0.55 | 0.63 | 0.70 | 0.43 | Lav | Lav | mc4 |
Third Lav | 0.79 | 0.43 | 0.74 | 0.60 | 0.72 | 0.49 | 0.51 | 0.38 | 0.48 | Lav | Lina | mc468 |
Fourth Lav | 0.83 | 0.68 | 0.58 | 0.70 | 0.66 | 0.58 | 0.61 | 0.53 | 0.20 | Lav | Lina | Lav |
Sixth Lav | 0.81 | 0.52 | 0.79 | 0.85 | 0.78 | 0.73 | 0.75 | 0.52 | 0.37 | Lina | Lav | mc468 |
Seventh Lav | 0.56 | 0.91 | 0.45 | 0.47 | 0.53 | 0.59 | 0.64 | 0.76 | 0.06 | Lav | mc4 | mc4 |
Ninth Lav | 0.56 | 0.83 | 0.62 | 0.48 | 0.68 | 0.67 | 0.72 | 0.76 | 0.28 | Lav | mc4 | mc468 |
First Lina | 0.73 | 0.44 | 0.78 | 0.66 | 0.69 | 0.55 | 0.54 | 0.38 | 0.63 | Lina | Lav | mc468 |
Fourth Lina | 0.59 | 0.40 | 0.7758 | 0.84 | 0.85 | 0.75 | 0.7756 | 0.42 | 0.27 | mc468 | Lina | Lina |
Third mc468 | 0.64 | 0.55 | 0.77 | 0.837 | 0.83 | 0.89 | 0.839 | 0.51 | 0.36 | mc468 | mc4 | Lina |
Second mc4 | 0.52 | 0.62 | 0.69 | 0.77 | 0.84 | 0.891 | 0.887 | 0.64 | 0.27 | mc468 | mc4 | mc468 |
Third mc4 | 0.53 | 0.779 | 0.71 | 0.64 | 0.775 | 0.778 | 0.84 | 0.80 | 0.18 | mc4 | mc4 | Lav |
Fourth mc4 | 0.59 | 0.65 | 0.57 | 0.67 | 0.65 | 0.79 | 0.85 | 0.70 | 0.21 | mc4 | mc468 | mc4 |
Correct rate | 75% | 100% | 100% |
Estimated information of single-trial brain waves in layer I of the aPC by ranking of wavelet correlations using two standard brain waves (set 1r).
Finally, it was examined whether the combination of data for two recording sites (layers I and III) affected the accuracy for the first candidates. Using this method, the accuracy (75%) of estimated information was maintained but not improved in standard Set 1 + 1′ and Set 1r + 1r’ (data not shown).
A new method is proposed for estimating the information of single-trial brain waves in fine temporal structures with a cross-trial variability by using a set of standard brain waves in a given category for each individual. In the oscillatory brain waves recorded in layer III or I of the aPC of the isolated whole brain of a guinea pig, the wavelet correlation analysis provided a 75% accuracy for the first candidate and a > 92% probability of including the correct information for the two upper candidates (Tables 5 and 7). The results support the validity of the proposed criteria for selecting standard brain waves with a wide variation for estimating different information in a given category.
The accuracy of this method was not affected by the information redundancy of signal sources, such as those resulting from olfactory receptors with overlapping tuning specificities and an experience dependency in layer I or from pyramidal cells with a stimulus dependency after the integration of signals from multiple cognate olfactory receptors in layer III (Table 8). Layer I brain waves comprising redundant signals exhibited a similar accuracy of estimated information and a slightly increased probability of including the correct information for the two upper candidates compared to layer III brain waves.
Information | Recoding sites | Estimated information | |||
---|---|---|---|---|---|
Lav | Lina | mc468 | mc4 | ||
Lav | Layer I (input) | 62.8% | 20.9% (e) | 9.3% (e) | 7.0% (e) |
Layer III (output) | 57.9% | 18.4% (e) | 1.3% (e) | 22.4% (e) | |
Lina | Layer I (input) | 0.0% (e) | 53.3% | 46.7% (e) | 0.0% (e) |
Layer III (output) | 7.7% (e) | 73.1% | 19.2% (e) | 0.0% (e) | |
mc468 | Layer I (input) | 0.0% (e) | 25.0% (e) | 75.0% | 0.0% (e) |
Layer III (output) | 0.0% (e) | 26.7% (e) | 73.3% | 0.0% (e) | |
mc4 | Layer I (input) | 4.5% (e) | 0.0% (e) | 13.6% (e) | 81.8% |
Layer III (output) | 0.0% (e) | 0.0% (e) | 30.8% (e) | 69.2% |
Correct and error rates (e) of estimated information in single-trial brain waves recorded in layers I and III of the aPC by the wavelet correlation analysis.
The redundancies of brain waves are attributable to two origins: information and signaling. In the olfactory system, the information redundancy changes through the signal pathway from the receptors to the higher cortical areas via signal integration in the third- or higher-order neurons and/or mutual inhibition [1, 11–13] for category [14] or elemental odor representation [15]. Unlike the >80% overlap of about 70 receptors for carvone enantiomers having similar odors [16], the quite different odors of Lav and mc468 evoked different amplitude receptor potentials in the olfactory epithelium and dissimilar brain waves in the anterior piriform cortex [1]. Nevertheless, the wavelet correlation analysis sometimes produced the highest correlation coefficients of Lav for mc468. The error rate of Lav for mc468 was 9.3% in layer I brain waves but was reduced to 1.7% in layer III brain waves (Table 8 and Figure 10), which is consistent with the change in the information redundancy from high to low stages between layers I and III. On the other hand, the error rate of mc468 for Lav was 0% in both layers I and III. For the single-compound odors, Lina and mc4 exhibited odor similarity-dependent changes in the error rates of the estimated information between layers I and III. The error rates of the single compounds for their original mixture odors (partially similar odor) increased between layers I and III (0 → 7.7% in Lina and 13.6 → 30.8% in mc4) and those of single compounds for their nonrelative mixture odors (dissimilar odor) decreased between layers I and III (46.7 → 19.2% in Lina and 4.5 → 0% in mc4). Notably, the error rates between these single compounds were 0% in both layers I and III. These results suggest a partial overlap of the elemental odors that are represented in the pyramidal cells in the aPC and are recorded in layer III as brain waves. The total error rates of Lina decreased in layer III compared to those of layer I (and vice versa for the correct rate), whereas those of mc4 increased.
Correct and error rates of estimated information in single-trial brain waves recorded in layers I and III of the aPC by the wavelet correlation analysis. These values are listed in Table 8.
The signaling redundancy originates from an identical temporal profile of different subsets of neurons tuned to distinct or shared information or from identical temporal profiles that are composed of multiple different profiles of various different subsets of neurons tuned to multiple distinct or shared information. The constant error rates of mc468 for Lina between layers I and III (both ~25%, Table 8 and Figure 10) are likely attributable to the signaling redundancy rather than the information similarity or information redundancy. Moreover, in the increased case, there was a threefold higher error rate of Lav for mc4 in layer III than layer I, whereas the error rates of Lav for Lina were almost constant between layers I and III.
Each brain system (e.g., a sensory, memory, decision, or motor system) is organized in a hierarchical manner from simple to complicated matters. The sensory system generates oscillatory activities between the related cortical regions and the thalamus, and the latter acts (except in the olfactory system) to gate the sensory input to the cortex and provides feedback from the cortical pyramidal neurons. In olfaction, transient oscillatory brain waves are observed in the aPC [5, 17–21]. Strong feed-forward inhibition [5, 22, 23] via the sensitive pathway from the olfactory bulb [24] and the other sensory thalamocortical circuit [25, 26] or higher olfactory centers [27] could induce oscillatory brain waves that would contribute to parts of the EEGs recorded at the respective positions on the human scalp, in analogy to these experimental animals. Such information-dependent temporal profiles of the EEGs may enable us to estimate in-brain information by comparison with a set of standard time-frequency power profiles of EEGs in each individual. To this aim, a wavelet correlation analysis of the brain waves in a guinea pig was conducted using standard brain waves with the proposed criteria and achieved an accuracy of 75% for the first candidates. This accuracy is attributable to the comparisons with standard single-trial responses in the wavelet time-frequency power profiles.
Conventional methods have focused only on some parts of the brain wave characteristics. For example, the FFT power spectra of sensorimotor EEGs [28, 29] or auditory EEGs [30] in specific frequency bands at a specific recording position were analyzed for the development of brain-computer interfaces. The Morlet wavelet convolutions for four-frequency band powers of the single-trial EEGs were analyzed to understand the cognitive control system via a priori estimation of information across three tasks [31]. By using the wavelet correlation analysis in the time-frequency power profiles at nine frequencies, these analyses could be improved in their subprocesses. Odor sensation [32, 33] and color-opponent responses [34] were also recorded in humans at Fz and an intermediate position between Oz and the inion, respectively, and they demonstrated informational differences in response amplitudes or profiles. Like EEGs in object recognition and those responsible for mental states, these EEGs are also subjects for the application of the wavelet correlation analysis for estimating in-brain fine information. Pain-related alpha-band desynchronization at contralateral-central electrodes (C2, C4, CP2, and CP4) and gamma-band synchronization at the ipsilateral-posterior electrodes (P3, P5, and so on) [35] are also good candidates for application. In animal models, the neural pathways of innate and learned fear responses have been revealed [36], and different pathways of stress relaxation using rose and hinokitiol odors were found [37, 38]. Therefore, determining their differing time-frequency power profiles would enable us to estimate the strengths of stress or relaxation in EEGs in humans. Future studies will focus on programming the wavelet correlation analysis for real-time estimates of in-brain information in humans.
We developed a new method for a similarity analysis and real-time estimates of in-brain information in single-trial brain waves by ranking the correlation coefficients in the wavelet correlation analysis. The wavelet correlation analysis with a set of standard brain waves provided the first candidate of estimated information with an accuracy of 75% with a > 92% probability of including the correct information for the two upper candidates, regardless of the information redundancy of signal sources. This method may be also useful for its applications to brain-machine interfaces or medical/research tools.
We would like to thank Dr. Mutsumi Matsukawa for his contributions to the development of the isolated whole-brain experimental system that enabled the recordings of odor-induced and nonolfactory origin-free brain waves. We are also grateful to Kiyo Murano for writing the computer software for wavelet transformation. This work was supported by grants (T.S.) from METI, Japan, and Grant-in-Aids for Scientific Research (B) #15H02730 (T.S.) from the MEXT, Japan.
aPC | anterior piriform cortex |
aPCvr | ventro-rostral region of the aPC |
EEG | electroencephalography |
EOG | electro-olfactogram |
FFT | fast Fourier transform |
LFP | local field potential |
LOT | lateral olfactory tract |
OR | olfactory receptor |
osci-LFP | oscillatory local field potential |
Steel, because of its numerous applications, is the most important material among any engineering materials. It is mostly used in tools, automobiles, buildings, infrastructure, machines, ships, trains, appliances, etc., due to its low cost and high tensile strength. Primarily, steel is an alloy of iron and carbon, along with some other elements. The prime material of steel is iron. Iron is commonly found in the Earth’s crust in the form of ore, generally an iron oxide, i.e., magnetite or hematite. The extraction of iron from iron ore is done by removing oxygen and then reacting it with carbon to form carbon dioxide. This process is called smelting. Iron has the ability to have two crystalline forms, i.e., face-centered cubic (FCC) and body-centered cubic (BCC), depending on the operation temperature. Fe-C mixture is also added with other elements to produce steel with enhanced properties. Manganese and nickel (Ni) in steel are added to increase its tensile strength and promote stable austenite phase in Fe-C solution, chromium (Cr) increases hardness and melting temperature, and titanium (Ti), vanadium (V), and niobium (Nb) also increase the hardness. There are two types of steel depending on the alloying elements. If the alloying elements are above 10%, it is referred to as high-alloy steel, and in case of alloying element with 5–10%, it is referred to as medium-alloy steel. If the alloying element in the steel is below 5%, it is called low-alloy steel. The density of steel varies from 7.1 to 8.05 g/cm3 according to the alloying constituents.
When 0.8% of carbon-contained steels (identified as a eutectoid steel) are cooled,
Martensite has a lesser density (as it expands at the time of cooling) than austenite does. As a result the conversion among them consequences a variation in amount. During the above process, growth occurs. Internal stresses as of this growth usually acquire the compressed crystal form of martensite and elongated form on the left over ferrite, along with a significant quantity of shear on the constituents. When quenching is not appropriately done, it can cause crack on cooling due to the internal stresses in a part. They cause interior
The carbon steels are composed of carbon and iron by means of carbon up to 2.1 wt%. At the same time, when the carbon content increases, steel has the capability to become harder as well as stronger by heat treating, though it undergoes less ductility. In spite of heat treatment, a higher carbon content also decreases weldability. In carbon steels, the higher carbon content lowers the melting point.
The classifications of carbon steel are on the basis of carbon content:
Low-carbon steel: carbon wt% is in the range of 0.05–0.30 (called plain carbon steel) [1].
Medium-carbon steel: 0.3–0.6% is the approximate carbon content [1]. It helps in balancing ductility and strength and also has superior wear resistance; it is used in automobiles [2, 3].
High-carbon steel: carbon content lies from 0.60 to 1.00% [1]. It has very high strength and is used for tools, edged tools, springs, and wires [4].
Ultrahigh-carbon steel: it has carbon% between 1.25 and 2.0 [1]. It can be tempered to immense hardness. It is used in various purposes like axles, punches, or knives.
Mild steel, well known as plain carbon, is at present the common variety of steel as it is cost-effective and offers material properties for a lot of applications. It contains carbon wt% in the range of 0.05–0.30, building it more malleable and ductile. It has comparatively low tensile strength, other than being contemptible and simple to produce; surface hardness can be improved by carburizing. Due to its ductile nature, the failure from yielding is less risky, so it is best applicable (e.g., structural steel). The density of mild or low steel is ~7.85 g/cm3 [5] and Young’s modulus is ~200 GPa [6]. Low-carbon steels include a smaller amount of carbon than other steels and are easy to handle as it is more deformable.
Carbon steels that successfully experience heat treatment contain carbon in between 0.30 and 1.70 wt%. The impurities of different
Alloy steel reflects a category of steel facilitated with the addition of different elements. In general, all steels are referred to as alloy steel, while the plain steel is composed of iron added up to 2.06 wt% carbon. However, the term “alloy steel” commonly refers to steels that are alloyed with elements other than carbon. The total wt% of the alloying elements can be up to 20% to provide the material enhanced properties like better wear resistance, strength, or ductility. Low-alloyed steels are distinguished by their lower content of alloys with total content below 5%, whereas in the case of high-alloyed steel, the total sum of elements can be in the range of 5–20%, with improved properties. Apart from the above alloyed steels, there are even unalloyed steels that carry very small quantity of alloys. High-alloyed steel contributes to high strength, toughness, hardness, and creep resistance at specific heat treatment temperature. It also advances machinability and corrosion resistance. In addition, it even strengthens the properties of other alloying elements.
The accumulation of certain alloying elements, such as manganese and nickel, can stabilize the austenitic structure, facilitating heat treatment of low-alloy steels. In the extreme case of austenitic stainless steel, much higher alloy content makes this structure stable even at room temperature. On the other hand, such elements as silicon,
Austenite is only stable above 910°C (1670°F) in bulk metal form. However, FCC transition metals can be grown on a face-centered cubic or diamond cubic [7]. The epitaxial growth of austenite on the diamond (100) face is feasible because of the close lattice match, and the symmetry of the diamond (100) face is FCC. More than a monolayer of γ-iron can be grown because the critical thickness for the strained multilayer is greater than a monolayer [7]. The determined critical thickness is in close agreement with theoretical prediction.
As the names suggest, austenite stabilizers are elements, which make austenite (of iron) stable at lower temperature, that would occur in pure iron. With enough amount of austenite stabilizer, you can have austenite stable at room temperature. Effectively, they decrease the austenitizing temperature of iron, in the Fe-C diagram.
Examples: Mn, Ni, C etc.
Manganese: in alloy steel, manganese is typically used in combination with sulfur and phosphorus. Manganese helps reduce brittleness and improves forgeability, tensile strength, and resistance to wear. Manganese reacts with sulfur, resulting in manganese sulfides which prevent the formation of iron sulfides. Manganese is also added for better hardenability as it leads to slower quenching rates in hardening techniques. Excess oxygen can be removed in molten steel by using manganese.
Nickel: austenitic stainless steels are most known for their high content in nickel and chromium. It is used to increase strength, hardness, impact toughness, and corrosion resistance. Nickel-alloyed steels are often found in combination with chromium, resulting in an even higher hardness.
By decreasing eutectoid composition and increasing eutectoid temperature, ferrite stabilizers are the elements which stabilize ferrite phase. Cr and Si are examples for ferrite stabilizers. Ferrite stabilizers are also called carbide former element. Stabilizing ferrite decreases the temperature range, in which austenite exists.
The elements, with the same crystal structure as that of ferrite (body-centered cubic—BCC), increase the A3 temperature and lower the A4 point. An increase in the amount of carbides in the steel is caused by decreasing the solubility of carbon in austenite by these elements. The following elements have ferrite-stabilizing effect: chromium, tungsten (W), aluminum (Al), molybdenum, silicon, and vanadium. Examples of ferritic steels are transformer sheet steel (3% Si) and F-Cr alloys.
Chromium: chromium is one of the most common alloying metals for steel because of its high hardness and corrosion resistance. Pure chromium is a gray, brittle, and hard metal with a melting point of 1907°C (3465°F) and a high-temperature resistance. In steel, hardenability is increased by the alloying chromium. Higher chromium contents up to 18% result in enhanced corrosion resistance. For example, stainless steel, which is one of the most popular steel alloys, uses at least 10.5% chromium, enhancing its resistance against water, heat, or corrosion damage. Chromium oxide does not spread and fall away from the material in contrast to iron oxide in unprotected carbon steel. It creates a film of dense chromium oxide on the surface that blocks out any further corrosion attacks.
Molybdenum: it is a silvery-white metal that is ductile and highly resistant to corrosion. It has one of the highest melting points of all pure elements—together with the elements tantalum (Ta) and tungsten. Molybdenum is also a micronutrient essential for life.
Carbide-forming elements form hard carbides in steels. Steel hardness and strength are increased by hard (often complex) carbides formed by the elements like tungsten, niobium, molybdenum, chromium, vanadium, titanium, zirconium (Zr), and tantalum. Examples of steels containing relatively high concentration of carbides are high-speed steel and shot work tool steels. During reaction with nitrogen in steel, carbide-forming elements also form nitrides.
Tungsten is a rare metal found naturally on the Earth almost exclusively combined with other elements in chemical compounds rather than alone. It was identified as a new element in 1781 and first isolated as a metal in 1783. Its important ores include wolframite and scheelite.
The free element is remarkable for its robustness, especially the fact that it has the highest melting point of all the elements discovered, at 3422°C (6192°F, 3695 K). It also has the highest boiling point, at 5930°C (10,706°F, 6203 K). Its density is 19.25 times that of water, comparable to that of uranium and gold, and much higher (about 1.7 times) than that of lead. Polycrystalline tungsten is an intrinsically brittle and hard material (under standard conditions, when uncombined), making it difficult to work. However, pure single-crystalline tungsten is more ductile and can be cut with a hard steel.
Alloy steel is added with a choice of
Alloy steels are categorized into low- and high-alloy steels. High-alloy steels would be more than 10 wt% of alloying elements in steel groups [1, 5, 8, 9]. The majority of alloy steels lie under the group of low alloy. The most common alloy elements include chromium, manganese, nickel, molybdenum, vanadium, tungsten, cobalt, boron, and copper.
Low-alloy steels are a group of ferrous materials that show improved mechanical properties compared to plain carbon steels, because of the alloying elements such as nickel, molybdenum and chromium. Through the development of specific alloys, low-alloy steel provides desired mechanical properties. Microstructure consists of ferrite and pearlite. Its properties are relatively soft and weak, although they have high ductility and toughness. Its various applications are auto-body components, structural shapes, sheets, etc. [2, 3, 5, 6, 10, 11, 12].
Some of the compositions of low-alloy steels are the following:
Cr 0.50% or 0.80% or 0.95%, Mo 0.12% or 0.20% or 0.25% or 0.30%, rest Fe | |
Mo 0.20% or 0.25% or 0.25% Mo or 0.042% S, rest Fe | |
Mo 0.40% or 0.52% C, rest Fe Ni 1.82%, Cr 0.50% to 0.80%, Mo 0.25% Cu, rest Fe Several low-alloy steels underwent normalizing and tempering in the manufacturing industries; however there is an increase affinity to a quenching and tempering action. Low-alloy steels are weldable, but pre-welding or post-welding heat treatment is essential to evade weld zone cracking issues. |
In high-alloy steel, the entire alloying element content is above 10 wt%. In stainless steels, the principally alloying element is Cr (≥11 wt%). It is greatly resistant to corrosion. Nickel and molybdenum addition adds to corrosion resistance. An important property of the highly alloyed steel is the capability of alloying elements to promote the creation of a certain multiple phases and stabilize it. These elements are grouped into four major classes as discussed in the previous section: (1) austenite-forming, (2) ferrite-forming, and (3) carbide-forming.
Some varieties of the high-alloy steels are the following:
Stainless steels: Fe-18Cr-8Ni-1Mn-0.1C characteristically is γ-alloy. It stabilizes austenite for its rising temperature range, where austenite subsists. It elevates the austenite-forming temperature (A1) and reduces the A3 temperature. Mostly, this type of steels underwent solution annealing type of heat treatment primarily specified for austenitic stainless steels. The main requirement for this treatment is to dissolve all the precipitated phases, mainly chromium-rich carbides, where the precipitate of M23C6 occurs in the range of 673–1173 K. For other stainless steels, it is recommended to maintain the solution annealing temperature in the range of 1273–1393 K.
Tool steel: it provides necessary hardness with simpler heat treatment and retains hardness at high temperature. The primary alloying elements are Mo, W, and Cr. These elements have wear resistance, high strength, and toughness but have low ductility. One of the primary heat treatments provided for tool steel is tempering that requires cautious preparation. Various complex tool steels like the high-speed steel need twice over tempering to convert austenite to martensite completely. High-speed steel (18 wt%W, 4 wt%Cr, 1 wt%V, 0.7 wt%C, 5–8 wt%Co, rest Fe) suits best for high-speed machining purpose, owing to secondary hardening. Besides, high-temperature annealing is performed with majorly ferritic structure to achieve a maximum bending strength of 4700 MPa. These types of steels achieve utmost hardness after first tempering, which is followed by second tempering that lowers the hardness to the desired working level. In some cases, the third temper is needed for secondary hardening of steels to make sure that some new martensite produced as a consequence of austenite conversion in tempering is efficiently tempered. This is a subject of individual selection and includes minimum extra cost.
High-entropy alloy steel: the essential elements of the high-entropy steels are Fe, Co, Ni, Cr, Cu, and Al. The cast microstructure expands from FCC to BCC phase along with the increase in Al content. The hardness in BCC phase is greater than FCC phase; in addition to it, the corrosion resistance is also superior in BCC phase. Some of the high-entropy alloy steels like Al-Fe-Cr-Co-Ni-Ti alloy coating was equipped by laser cladding, and the effects of annealing temperature (873, 1073, and 1473 K) on structure and its properties were studied. The consequences illustrate that the intermetallic precipitation compounds in the coating are efficiently repressed through laser cladding by means of fast solidification, and the microstructure of the coating forms dendrite structure of BCC, having superior hardness (~698 HV). As a result, the grain size of the coating rises somewhat, and the microhardness reduces slightly, following various annealing temperatures at a range of 1073–1373 K. This specifies that the elevated temperature stability of the structure and microhardness of the coating are superior. Al and Fe are improved in dendritic boundary, while Co, Ni, Ti, and Cr are enhanced in interdendritic boundary. In addition, the degree of segregation rises with the enhancement of annealing temperature.
Twinning-induced plasticity (TWIP) steel: in TWIP steel (>20 wt%Mn, <1 wt%C, <3 wt%Si, <3 wt%Al, rest Fe) high-temperature thermomechanical heat treatment provides a strength greater than1000 MPa. The examination of the solution heat treatment of hot-rolled TWIP steel of the three various compositions (Fe-30Mn-3Si3Al, Fe-25Mn-4Si-2Al, and Fe-30Mn-4Si-2Al) reflected that prolonging the time of holding temperature can enhance the elongation through no change observed in strength. Prolonging the holding time facilitates both the production of additional annealing twins to amplify their areas of boundary and the boost in the number of twin boundaries that are favorable for the corrosion resistance creep and fracture.
Hadfield steel: in Hadfield steel (11–14 wt%Mn, 1–1.4 wt%C), a fully austenitic phase is obtained with a strength level of 1000 MPa. High-alloy tool steel (5 wt%Mo, 6 wt%W, 4 wt%Cr, 0.3 wt%Si, 1 wt%V, rest Fe) is provided with austenitizing, quenching, and tempering treatment to achieve a maximum hardness of 1200–1400 HV. The heat treatment processing of Hadfield manganese steel means dissolving the carbide precipitates at higher temperature, followed by fast cooling to attain austenitic carbide-free grains which is desired to be the preferred microstructure for the commercial applications.
High-temperature homogenization, complete annealing, normalizing, tempering, etc. are the usual methods in heat treatment process of steel. But there are certain modified ways of processing routes in order to enhance the mechanical properties [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. The main objective of heat treatment in steel is to upgrade the mechanical properties like strength, toughness, impact resistance, etc. It is to be noted that thermal and electrical conductivities are changed to some extent, whereas Young’s modulus remains unchanged. Iron has a better solubility for carbon in the
Some of the newly introduced high-alloyed steels like TWIP steel show excellent mechanical properties, depending on the adoption of advanced heat treatment processes. In some processes the fabricated steels are first homogenized to ~1373 K for 1 hour, followed by hot rolling at 1273 K. The steels are then cooled in the furnace and then rolled at room temperature (as shown in Figure 1). Due to the above heat treatment, the presence of duplex phases of austenite and ferrite is observed. The rolling effect contributes in grain size reduction and hence helps in enhancing the strength of the steel. Additionally, due to the high-temperature rolling, there is also an occurrence of twins on the austenitic grains that also increases the strength of the metal. The above modification in the microstructure resulted in the improved tensile properties with 1000 MPa ultimate tensile strength and up to 60% elongation [13].
Illustrating the processing routes of TWIP steel.
Recently, Mazaheri et al. suggested a cold rolling, followed by various intercritical annealing techniques for the production of ultimate ultrarefined-grained steel [22]. The microstructure contains ferrite-martensite duplex steel with excellent mechanical properties. In this processing route, the fabricated steel was first heated to austenitizing temperature, i.e., 880°C for 1 hour. Then it was annealed intercritically at ~770°C for 100 minutes trailed by water quenching (as shown in Figure 2). The steel was water cooled to acquire the desired microstructure of ferrite and martensite structures, and on further annealing the aimed ultrafined-grained microstructure was achieved. The achieved strength (UTS) is ~1600 MPa with 30% elongation [13].
Thermomechanical processing routes of dual-phase steel.
The temperature of deformation also plays a vital role in influencing the refinement of the microstructure through hot deformation. In Figure 3a the martensitic phase is dominated, resulting in ultrafined grains due to dynamic recrystallization (DRX) of ferrite grains. In the processing of steel as shown in Figure 3b, the martensitic content is above 30% which contributes to the strength of the steel by the varying the degree of deformation. As compared to the routes of Figure 3a and b with Figure 3c, the DRX is not necessary for the formation of ultrafined grains; the warm temperature deformation followed by intercritical annealing can also result in the formation of similar structure. Therefore, the warm rolling and high rate of intercritical annealing and high rate of cooling significantly affect the microstructural properties of the steel.
Various heat treatment processes owing to different ways of thermomechanical treatments in steel.
There are various strengthening mechanisms affecting the strength of the steel. By following specific thermomechanical treatment, the occurrence of twins enhances the strength of the steel. Twinning-induced plasticity steels are FCC crystal-structured steels. The appearance of the crystallographic twins greatly depends on the stacking fault energy (SFE), and the SFE of the steel is controlled by the rate of heating treatment. Temperature is directly proportional to SFE. Low SFE (below 20 mJ/m2) results in the conversion of austenite to martensite (i.e., TRIP effect), whereas high SFE (above 20 mJ/m2) gives TWIP effect (formation of twins). The dislocation generated during the deformation is obstructed by the twins and, therefore, increases the strength of the steel [32, 33].
Thus by adopting this technique, the microstructural modification takes place by the combined effect of mechanical and thermal energy. There are also iterative thermomechanical processes where percent of deformation is applied prior to heat treatment (Table 1). This process also contributes to the resistance of corrosion with respect to the orientation of the grain [3, 14, 21, 23].
Steel type | Maximum forging temperature (°C) | Burning temperature (°C) |
---|---|---|
Carbon steel | 1200 | 1349 |
Nickel steel | 1249 | 1380 |
Chromium steel | 1200 | 1370 |
Nickel-chromium steel | 1249 | 1370 |
Stainless steel | 1280 | 1380 |
TWIP steel | 1200 | 1350 |
High-speed steel | 1280 | 1400 |
Various steels corresponding to different ranges of deformation temperature.
The above heat treatments are aimed to enhance the specific properties of the high-alloyed steel to get rid of unwanted properties. Some of the microstructures evolved during processing are given in Figure 4.
Various microstructures of high-alloy steels.
The behavior of steel in exterior load describes its mechanical properties. Plastic deformations are supported by the movement of dislocation and the presence of twins, and precipitates hinder the motion of dislocations and thereby increase the strength of the steel. Mechanical properties are associated with the yield stress, separating the elastic and plastic regions, where the activity of dislocation extends [15, 16, 17, 30, 31, 32]. Pinning of dislocations by random obstruction is controlled by the misfit and size of the particles. In general, larger SFE promotes dislocation gliding, which enables the dislocation to move freely. On the other hand, the smaller SFE increases the area between the two partials, thereby making the motion of dislocation difficult and resulting in the piling up of dislocation. For the duration of the dislocation union, the partials must reconnect to prevail over the obstruction [30, 31, 32, 33, 34]. The opposition of steel to plastic deformation reduces with rising SFE, and for this reason the SFE should be lowered to reinforce the strength. Based on the observation, SFE is regulated by alloyed elements in the steel for preferred enhanced properties like strength, hardness, or rate of work hardening.
High-alloy steels have vast applications such as:
Stainless steel: it has excellent corrosion properties and is used in structural applications, refrigerator, freezers, food packaging, etc.
Tool steel: used in dies, shear blade, rollers, cutting tools, etc.
TWIP steel: used in automobiles, ship building, infrastructure, railways, aircrafts, etc.
High-entropy steel: used as structural material in low-temperature applications due to its high toughness.
Hadfield steel: used in railways, structural applications, shafts, gears, housing, cables, etc.
High-speed steel: used as cutting tool materials due to its high hardness like drilling machine, blades, etc.
High-alloyed steels are complex alloys, along with desired chemical composition and multiple phased microstructures through various heat treatment processes. Various strengthening mechanisms through controlled heat treatment techniques are adopted to achieve excellent mechanical properties. The chapter examines the advanced methods used in the field of heat treatment routes for high-alloyed steel and focuses on their structure-property relation. The high-alloy steels acquire its enhanced mechanical properties from the modified microstructures of austenite, ferrite, martensite, and some carbides. Ferrite and austenite provide the formability, whereas martensite provides strength to the steel in addition to the low-temperature transforming phases like bainite and retained austenite to achieve better combinations of mechanical properties. The advanced thermomechanical treatments used for high-alloy steels aim to explore the possible phases that contribute to the mechanical properties. In thermomechanical routes aims on heat treatment as the microstructural qualities required for the steels are mainly achieved by post-deformation controlled heat treatment processes. From the above discussions, it can be concluded that the microstructure and its properties are based on variation in chemical composition and processing conditions. Determined by latest demands for the performance of the high-alloy steel in various applications, the progress of thermomechanical processing is introduced.
High-alloy steel has undergone significant evolution through time. Around 70% is used in various applications. These steels are highly demanding as they display various environmental, chemical, physical, and mechanical properties. Here the different proportions of alloying element in steel provide various mechanical properties. As can be seen from the foregoing, high-alloy steel plays an important role in the building and construction industries as well as in automotive industries. High-alloy steel offers economy, high performance, corrosion resistance, high strength, durability, lightweight and high performance under extreme conditions, and its wide variety of products for desirable applications.
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
",metaTitle:"Open Access Publishing Fees",metaDescription:"Open Access Publishing Fees",metaKeywords:null,canonicalURL:"/page/OA-publishing-fees",contentRaw:'[{"type":"htmlEditorComponent","content":"As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\\n\\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\\n\\nOAPF Publishing Options
\\n\\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\\n\\nServices included are:
\\n\\nSee our full list of services here.
\\n\\nWhat isn't covered by the Open Access Publishing Fee?
\\n\\nIf your manuscript:
\\n\\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\\n\\nOpen Access Funding
\\n\\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\\n\\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\\n\\nAdded Value of Publishing with IntechOpen
\\n\\nChoosing to publish with IntechOpen ensures the following benefits:
\\n\\nBenefits of Publishing with IntechOpen
\\n\\nAs a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
\n\nOAPF Publishing Options
\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\n\nServices included are:
\n\nSee our full list of services here.
\n\nWhat isn't covered by the Open Access Publishing Fee?
\n\nIf your manuscript:
\n\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\nOpen Access Funding
\n\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at oapf@intechopen.com for further details or assistance.
\n\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\nAdded Value of Publishing with IntechOpen
\n\nChoosing to publish with IntechOpen ensures the following benefits:
\n\nBenefits of Publishing with IntechOpen
\n\n