Studies of emotions recognition from EEG recordings with regularity-based entropy indices.
\\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!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 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 261 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!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"474",leadTitle:null,fullTitle:"Advances in Ceramics - Synthesis and Characterization, Processing and Specific Applications",title:"Advances in Ceramics",subtitle:"Synthesis and Characterization, Processing and Specific Applications",reviewType:"peer-reviewed",abstract:"The current book contains twenty-two chapters and is divided into three sections. Section I consists of nine chapters which discuss synthesis through innovative as well as modified conventional techniques of certain advanced ceramics (e.g. target materials, high strength porous ceramics, optical and thermo-luminescent ceramics, ceramic powders and fibers) and their characterization using a combination of well known and advanced techniques. Section II is also composed of nine chapters, which are dealing with the aqueous processing of nitride ceramics, the shape and size optimization of ceramic components through design methodologies and manufacturing technologies, the sinterability and properties of ZnNb oxide ceramics, the grinding optimization, the redox behaviour of ceria based and related materials, the alloy reinforcement by ceramic particles addition, the sintering study through dihedral surface angle using AFM and the surface modification and properties induced by a laser beam in pressings of ceramic powders. Section III includes four chapters which are dealing with the deposition of ceramic powders for oxide fuel cells preparation, the perovskite type ceramics for solid fuel cells, the ceramics for laser applications and fabrication and the characterization and modeling of protonic ceramics.",isbn:null,printIsbn:"978-953-307-505-1",pdfIsbn:"978-953-51-4464-9",doi:"10.5772/985",price:159,priceEur:175,priceUsd:205,slug:"advances-in-ceramics-synthesis-and-characterization-processing-and-specific-applications",numberOfPages:534,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:null,bookSignature:"Costas Sikalidis",publishedDate:"August 9th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/474.jpg",numberOfDownloads:100803,numberOfWosCitations:231,numberOfCrossrefCitations:56,numberOfCrossrefCitationsByBook:13,numberOfDimensionsCitations:199,numberOfDimensionsCitationsByBook:21,hasAltmetrics:1,numberOfTotalCitations:486,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 18th 2010",dateEndSecondStepPublish:"November 15th 2010",dateEndThirdStepPublish:"March 22nd 2011",dateEndFourthStepPublish:"April 21st 2011",dateEndFifthStepPublish:"June 20th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7,8",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"42599",title:"Prof.",name:"Costas",middleName:null,surname:"Sikalidis",slug:"costas-sikalidis",fullName:"Costas Sikalidis",profilePictureURL:"https://mts.intechopen.com/storage/users/42599/images/1711_n.jpg",biography:"Dr. Costas Sikalidis, born in Thessaloniki Greece in 1948, matriculated in the Chemistry Department at the Aristotle University of Thessaloniki in 1966. After earning his BSc, he served for three years as an officer in the Hellenic Army. He pursued postgraduate studies in Ceramic Technology at the University of Northstaffordshire, UK and completed training & professional development in the British Ceramic Industry. He worked as a production manager at Philkeram-Johnson, subsidiary of Johnson Tile manufacturers. In 1981 he joined the Department of Chemical Engineering as a faculty member holding a PhD in Chemical Engineering. A Professor and Head of the Laboratory of Industrial Inorganic Raw Materials and Industrial Ceramics, he has published more than 70 papers in peer-reviewed scientific journals, presented his research in more than 80 International Conferences, received several patents and has been a reviewer in 10 journals. Dr. Sikalidis has mentored more than 18 MSc graduates and produced 2 PhD graduates, whereas he has earned funding via a number of competitive European research grants. 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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"}}]},chapter:{item:{type:"chapter",id:"77676",title:"Applications of Titanium Dioxide Materials",doi:"10.5772/intechopen.99255",slug:"applications-of-titanium-dioxide-materials",body:'Titanium dioxide (TiO2) is an inorganic substance that is used extensively as a white pigment. Compared with many other inorganic pigments, TiO2 has the advantages of high stability, being non-toxic, and low cost. TiO2 have three polymorphs: anatase, rutile and brookite, but only anatase and rutile crystal forms have been useful as pigment. Both anatase and rutile crystals have very high refractive indices, and their particles can scatter visible light almost completely [1]. The optimum particle size of TiO2 for pigment applications is around 250 nm. In the early application of TiO2 as pigment, it was found that paint faded more rapidly than others when painted films were exposed to the Sun and ultraviolet (UV) light. Coating with inorganic compounds such as alumina or silica suppressed the catalytic activity on the surface and improve the weather resistance, leading titanium dioxide in wide applications as white pigment. Global titanium dioxide pigment sales were about 6 million tons in 2017 and the growth trend of global titanium dioxide pigment sales is continuing over the recent years [2].
In the 60s of the last century, scientists studied the photo-induced phenomena on the solids of TiO2 and ZnO under UV light irradiation [3, 4, 5]. In the early 1970s, research on photocatalysis by TiO2 got wide attention due to the historic discovery of the electrochemical water splitting by use of TiO2 [6]. In the 1990s, photocatalytic research of TiO2 had made progress in the practical applications of TiO2 in the decomposition of harmful organic materials [7, 8]. A function of super hydrophilicity of TiO2 was also discovered [9]. Since the beginning of this century, nano-structured TiO2 has attracted extensive interests. When the particle sizes of TiO2 are reduced down to the nano-meter scale (generally in 1–100 nm), the surface characteristics and surface areas of TiO2 have changed dramatically. The new or enhanced physical and chemical properties of nano-structured TiO2 begin to emerge. The photocatalytic property of nano-structured TiO2 has been greatly enhanced because of the changes in the surface characteristics and surface areas. Quantum effects of nano-structured TiO2 can also have a role to play, affecting its photocatalytic, optical or electronic properties. As the results of academic and industrial research in recent years, enormous progresses have been made in the preparation, characterization, and scientific understandings of nano-structured TiO2. Nano-structured TiO2 have begun to find applications in a wide range of areas including electronic materials, energy, environment, health & medicine, sensors, catalysts, etc.
TiO2 pigment is industrially produced from titanium containing ores by using a Chloride or Sulfate process [1, 10]. Nano-structured TiO2 are made in the different ways, depending on the material characteristics required for the specific applications. A number of innovative fabrication methods of nano-structured TiO2 materials have been developed and are used for different applications. These methods can be broadly classified as the liquid phase or the gas phase methods. Nano-particles, nano-wires, nano-tubes, two or three-dimensional nano-structured TiO2 materials can also be fabricated for different applications [11, 12, 13, 14, 15, 16, 17, 18].
This chapter will discuss and highlight the recent development of applications of titanium dioxide as pigment and as functional materials in the areas of energy, environment, catalyst, and biomedicine.
The main use of titanium dioxide is white pigment, because it absorbs almost no incident light in the visible region of the spectrum (380–700 nm). Titanium dioxide has a strong light scattering power, and scatters incident light in three ways: surface reflection, refraction and diffraction in the crystal [1]. When the refractive index difference between titanium dioxide and medium increases, the reflected light increases and complies with Eq. (1):
np and nm are the refractive index of pigment and medium, respectively [1]. Titanium dioxide has a high refractive index (refractive index of rutile and anatase titanium dioxide is 2.70 and 2.55 respectively) [19]. These high refractive index values enable the rutile and anatase TiO2 pigments to have much greater hiding power in coatings or in plastics, making TiO2 to be a much better pigment than the other chemical substances. Therefore, under the same conditions, only less titanium dioxide is needed to form a coating, which is white and opaque. Studies have shown that the optical properties of titanium dioxide pigments are related to their particle size, and the optimum of particle size of pigmentary titanium dioxide is around 250 nm [1].
Pigmentary TiO2 is inert, non-toxic, stable and less costly. Over 50 percent of all TiO2 pigment produced is consumed by the coatings industry, and approximately a quarter by the paper industry. Eleven per cent goes into plastics; remaining a few percent into inks and other end-uses [20]. Titanium dioxide particles optimized with particle size and surface treatments have excellent hiding power, brightness, and other important features such as resistance to chemical degradation. Rutile pigment is more resistant to UV light than anatase, and is preferred for paints, plastics, especially for the applications in outdoor conditions. Anatase pigment is less abrasive and is used mainly in indoor paints and in paper manufacture. TiO2 is surface treated with one or more inorganic oxides such as alumina, silica, zirconia or a combination of these inorganic oxides, and organic compounds such as polyhydric alcohol to have the required properties of dispersion, photoactivity, and opacity required for a specific application [21].
In coating applications, a relatively high quantity of TiO2 pigment must be used to achieve desirable hiding effect on the coating subtracts, because coatings of titanium dioxide are usually in the form of very thin layers. The pigment volume concentration (PVC) is practically used to specify the amount of TiO2 in a coating. Different types of paints containing TiO2 pigment will have different levels of PVC, depending on different coating applications. TiO2 coatings are used to cover a wide range of surfaces, including indoor and outdoor building, wood products, metal objects, domestic and industrial equipment [21].
In plastics applications, titanium dioxide pigment is used to opacify plastic materials. In some applications, TiO2 is used to improve photodurability. The requirements for TiO2 in plastics are good dispersibility in a polymer system and good heat stability. Hydrophobic organic surface treatments on the pigments are utilized to facilitate their dispersion in the viscous molten plastic resin. These are often silicone oils and other organic compounds for specialized uses. In many plastics applications, a blue undertone is also desirable to mask an intrinsic yellowness in the color of the resin or a slight degradation that occurs during the high-temperature processing. For this reason, plastics pigments often have a smaller crystal size than those for coatings applications [19].
The amount of titanium dioxide used in paper industry is the third largest after coating and plastic industries [20]. Although other white pigments can be used in paper industry, the production of high quality papers must use titanium dioxide as pigment. Titanium dioxide imparts desirable brightness and opacity to high-quality papers. Papers containing titanium dioxide pigment have high strength and have appearance to be white, shiny, thin and smooth. Because photochemical stability is not as critical in paper as in paint, both anatase and rutile pigments are widely be used in paper industry.
In inks applications, performance requirements for TiO2 pigment are different from coatings, plastics and paper. Inks are usually applied to produce a much thinner film on a surface than a general coating. It is very important to choose titanium dioxide particles with good shape, suitable size and size-distribution, smooth surface and non-angular. The type of TiO2 can also affect the rheology, abrasiveness, gloss and redispersibility for ink products and applications.
TiO2 is also widely used as a pigment for coloring of different products in pharmaceuticals and cosmetics industries. The characteristics of titanium dioxide provide interesting colors and allow new properties to pharmaceuticals with very small amounts of pigments. There are many products in this field that contain titanium dioxide, including: shampoos, creams, sunscreens, toothpaste, etc. [10].
With the special physical and chemical properties, nano-structured titanium dioxide has shown a number of promising application prospects in energy generation and storage. These include: solar cells, hydrogen production, and lithium battery [22, 23, 24].
The solar energy is a clean, abundant and renewable energy [25]. The current technology for the conversion of sunlight to electrical power is predominately silicon-based solid state solar cells. In recent years, the new semiconducting material-based solar cells have emerged to offer the possible alternative photovoltaic technology with prospect of cheap fabrication and flexibility [26, 27, 28]. Nano-structured TiO2 has been the main semiconducting material for this new generation of solar cells. In this technology, an electron sensitizer absorbing in the visible is used to inject charge carriers across the semiconductor-electrolyte junction into TiO2 to enhance the conversion efficiency from solar energy, because TiO2 with its band gap of 3.2 electronvolt (eV) absorbs only the ultraviolet part of the solar energy. This type of solar cells is therefore called dye-sensitized solar cells (DSSCs). The dye-sensitized solar cells (DSSCs) have exhibited high performance and have the potential to be low-cost [29, 30, 31, 32, 33].
Figure 1 illustrates the working principle of a dye-sensitized solar cell. The dye-sensitized solar cell consists of two electrodes, a dye-sensitized nano-structured TiO2 mesoporous layer, and a liquid electrolyte containing redox system (
Working principle of a dye-sensitized solar cell.
The nano-structured TiO2 mesoporous layer in a dye-sensitized solar cell has a much larger surface area available for the dye-chemisorptions. The kinetic processes occurring in a dye-sensitized solar cell have been profoundly changed as a result of using nano-structured TiO2. Solar energy-to-electricity conversion efficiencies of DSSCs have been increased. The record for the highest certified single cell and DSSCs module efficiencies are 11.9% and 8.8%, respectively [34].
More recently, TiO2 is used in a new type of solar device so-called pervoskite solar cells. As in DSSCs, TiO2 is used as a mesoporous layer. However, instead of using organic dye in DSSCs, organic lead complex (for example, CH3NH2PbI3) is used to inject electrons into the conduction band of TiO2. In a short period of the recent few years, the reported efficiency of pervoskite solar cells was 9.7% initially, and then 12.0% [35, 36]. Further progress was made with efficiencies above 15.0% [37]. The record for the highest certified single cell and minimodule efficiencies are 20.9% and 16.0%, respectively [34].
In 1972, Fujishima and Honda discovered the phenomenon of photocatalytic splitting of water on a TiO2 electrode under UV light [6, 38, 39]. Compared with other photocatalysts, TiO2 is much more promising as it is stable, non-corrosive, environmentally friendly, abundant and cost effective. Figure 2 illustrates the mechanism of the photocatalytic hydrogen production by TiO2 semiconducting materials. When excited by photons which have energy equal to or higher than their band gap (
Illustration of mechanism of photocatalytic hydrogen production by TiO2.
The photo-generated (e−) and (h+) in TiO2 can recombine, releasing energy in the form of heat or photons. The photo-generated (e−) and (h+) that migrate to the surface of TiO2 without recombination can reduce and oxidize H2O molecules adsorbed on the surface of TiO2 to generate H2 and O2.
As can be seen in Figure 2, the conduction band level of TiO2 is more negative than the hydrogen production level (
Despite many advantages of using TiO2 for photocatalytic hydrogen production, the efficiency using solar energy for water-splitting by TiO2 is still low, and is currently not been used for industrial scale of hydrogen production. The low energy conversion efficiency of TiO2 in water-splitting is believed to be caused by the wasteful recombination of electron/hole pairs, backward reaction of combining hydrogen and oxygen into water, and limitations for TiO2 to utilize visible light due to its large band gap. Research has been carried out to produce nano-structured TiO2 with a narrower band gap in order to utilize visible-light energy more efficiently. Progresses have been made in modifying the band gap of nano-structured TiO2 by means of metal loading, ion doping, metal ion-implantation, dye sensitization and composite TiO2. Noble metals, such as Pt, Au, Pd, and Ag, have been reported to be very effective in enhancing TiO2 photocatalysis [40, 41, 42, 43]. Carbon-doped nano-structured TiO2 have showed much more efficient water splitting under visible-light illumination [44]. A study using a dye sensitizer for photocatalytic hydrogen production was investigated [45]. A visible light absorber, C3N4, has been coupled to many wide-band gap semiconductors to improve solar harvesting. A 50 wt % C3N4/TiO2 junction was found to double H2 evolution compared to pure C3N4 under visible irradiation [46].
Lithium-ion batteries are a type of rechargeable batteries commonly used in consumer electronics. Lithium ion battery system and technology has been a revolutionary change in the field of power supply battery. Anode materials based on titanium oxides are the promising candidates as alternative materials to carbonaceous anodes due to advantages in terms of cost, safety and toxicity [47, 48]. TiO2 also exhibits excellent structural stability, high discharge voltage plateau (more than 1.7 V versus Li+/Li), and excellent cycling stability [49, 50].
Typically the Li+ insertion–extraction reaction for TiO2 polymorphs occurs according to reaction (3):
x can range between 0 and 1, depending strongly on the polymorph, particle size, and morphology of TiO2. The maximum theoretical capacity is 335 mAh g−1 which corresponds to x = 1. This makes TiO2 a highly competitive alternative to graphite anodes having a theoretical capacity of 372 mAh g−1 [51, 52, 53]. However, TiO2 has limitations, such as low capacity, low electrical conductivity, and poor rate capability. Strategies have been developed to address the issues of TiO2-based anodes. These include the use of multi-dimensional nanostructured TiO2, composite and coating materials, and element doping.
One dimensional anatase TiO2 nanofiber anodes were used as an anode active material in Li ion batteries and exhibited a high lithium storage capacity, a stable cycle life, and good rate capability [54]. Two dimensional TiO2 nanosheets have been shown to exhibit the superior capacities, improved cycling stability and rate capabilities, owing to unique exposed facets, shortened path, and reserved porous structures [55, 56, 57]. Nanostructured TiO2 is a low voltage insertion host for Li and a fast Li insertion/extraction host [58, 59]. These characteristics provide nanostructured TiO2 a potential anode material for high-power Li-ion batteries. Studies on the use of nanostructured TiO2 as anode with LiCoO2 cathode demonstrated specific capacity of 169 mAh g−1 [60]. Xu, et al. investigated electrochemical performance of TiO2-coated LiCoO2 and LiMn2O4 in different potential regions [61]. Mechanically blended composite of nanosized TiO2 and carbon nanotubes (CNTs) has been used as potential anode materials for Li-ion batteries. It was found that the TiO2/CNTs nanocomposites exhibited an improved cycling stability and higher reversible capacity than CNTs [62, 63]. Metal oxide coatings containing TiO2 can efficiently improve the capacitive performance of the materials through synergistic effects in an electrode system [64, 65, 66, 67].
Excitation of TiO2 with UV light with energy greater than the band gap (>3.2 eV) promotes electrons from valence band into the conduction band and generates electron/hole pairs [68, 69]. Figure 3 illustrates the mechanism of generating reactive radicals from TiO2 under irradiation of UV light. The conduction band electrons e− can reduce molecular oxygen to generate (O2•−) superoxide radicals, and valance band holes h+ is positive enough to generate (OH•) radicals from H2O or OH− on TiO2 surface. OH• radicals have the strongest oxidation potential. Superoxide radicals (O2•−) have moderate oxidation potentials, but their diffusion distances can reach up to hundreds of micrometers [70]. Both radicals are very reactive, and they attack the organic matter present or near the surface of TiO2 to degrade toxic and bio-resistant compounds or species into CO2, H2O, etc. [69, 71].
Mechanism of generating reactive radicals (OH•) and superoxide (O2•−) from TiO2 under irradiation of UV light.
The generation of reactive radicals (OH•) and (O2•−) is affected by the crystalline state, and properties such as surface area and particle size. Although anatase and rutile have the similar band gaps, anatase has shown to have more rapid rate in photo-degradation of organic or bio-resistant compound than rutile [72, 73]. Therefore, nano-structured anatase TiO2 is often used as a catalyst in photo-degradation applications.
One application, which is commercially successful, is the nano-structured TiO2 material for self-clean and antibacterial uses [68, 74, 75]. Many nano-structured TiO2 material based products have been used as construction materials [76, 77, 78, 79, 80, 81, 82]. Self-clean application is based on the actions of sunlight, rainwater, and photocatalytic properties of TiO2. Under the irradiation of sunlight, adsorbed organic materials like oil can be decomposed by hydroxyl radicals on the surface of TiO2. Because of the hydrophilic property of TiO2 surface, contaminates and dust can be washed away off by rainwater. Tiles containing nano-structured TiO2 have been used to construct photocatalytic surface to decompose bacteria and viruses on the surface or bacteria floating in the air as they come in contact with surface.
Studies have shown that the photocatalytic properties of TiO2 can sometimes be enhanced by doping TiO2 with different elements. For example, TiO2 nano-particles containing Ag+ have been widely used in antibacterial plastics and coatings [83, 84, 85]. Fe or Sb-doped TiO2 have been used to make coatings with high antibacterial property [79, 80].
Nano-structured self-clean glass is now an important commercial product. Pilkington Glass has developed the first self-cleaning windows. The window glass is coated with a very thin and transparent TiO2 layer to have the properties of photocatalysis and hydrophilicity on the glass surface. Photocatalysis of TiO2 break down the organic dirt adsorbed onto the window in sunlight, and the decomposed organic species is washed away efficiently by rain or other water in the form of thin layer instead of droplets [86].
Another important application of nano-structured TiO2 is in the water-treatment, utilizing its photocatalytic properties [87, 88, 89, 90, 91, 92]. Research of using nano-structured TiO2 for water-treatment has been very active in recent years. TiO2 has been used in the photocatalytic decomposition of organic dyes in waste water, and organic pollutants such as pesticides, dyes and pharmaceuticals in other contaminated water [93, 94]. The photocatalytic decomposition of organic matters in water are all based on the mechanism of the generation of highly reactive radicals (OH•) and superoxide ions (O2•−) in TiO2 under UV irradiation, as illustrated in Figure 3. TiO2 has been considered to be the best choice to be used as photo-catalysts, as TiO2 is chemically inert, and cheap to manufacture and to apply.
The complete separation and recycling of TiO2 fine particles is important for the practical applications. A number of innovative methods have been developed for this purpose. For example, fixing TiO2 nano-particles on supports such as glass plates, aluminum sheets, and activated carbon are investigated to recycle the catalyst [95], or developing TiO2 catalyst system which can be separated from reaction liquid by applying external magnetic field [96, 97].
Because TiO2 and many other semiconductors have the large band gaps, the application of photocatalytic water treatment using TiO2 is limited by its relatively low efficiency. To improve photocatalytic efficiency of TiO2 for water treatment, as well as other photocatalytic applications, Enormous research has been carried out to extend the photocatalytic response of TiO2 into the visible range [98]. One of the strategies for improving photocatalytic efficiency for water treatment is to modify the band gap of TiO2 by incorporation of other ions into TiO2 structure, through metal and non-metal doping, metal implantation, noble metal loading, and others [99, 100, 101, 102, 103, 104].
TiO2-based composite materials have been widely used as catalysts [105, 106, 107]. TiO2 is used as support in commercial V2O5-WO3/TiO2 catalysts for the selective catalytic reduction (SCR) of NOx. In SCR technology, highly undesirable NOx acid gas emissions from various industrial sources are reduced to harmless N2 and H2O. The V2O5-WO3/TiO2 catalysts are widely used in commercial applications because of their excellent thermal stability and lower oxidation activity for the conversion of SO2 to SO3 [108, 109]. The V2O5-WO3/TiO2 catalysts have become the most widely used industrial catalysts for these SCR applications since the introduction of this technology in the early of 1970s [110].
TiO2 has the potential to induce the reductive chemical transformation. The reductive photocatalysis of ethyne and ethene have been reported [111, 112, 113]. TiO2 have been used as a useful catalyst for the reduction of carbonyl compounds such as aldehydes or ketones, nitro compounds, imines and for the some of the chemical transformations involving redox processes. Photocatalysis on TiO2 is a light-driven redox reaction. Redox reactions can be induced by electrons (e−) generated in conduction band (CB) and holes (h+) simultaneously generated in valance band (VB) under the irradiation of light. The electrons in the conduction band are readily available for transferring while the holes in the valence band are open for donations [114]. The photocatalytic reduction of an electron acceptor can be carried out in the presence of a large excess amount of electron donors such as alcohols or amines, which are used to scavenge (h+). Oxygen (O2) is a competitive electron acceptor, and can influence the reduction reaction. Therefore, the reductive chemical transformation should be generally performed in an O2 free environment. Under these conditions, a photocatalytic reduction proceeds through transferring electrons (e−) in CB or trapped at surface defects of TiO2 into the organic molecules adsorbed on TiO2 surface. The photocatalytic reduction of aldehydes, nitro compounds, and imines have been reported. Aromatic aldehydes and ketones were reduced to the corresponding alcohols using TiO2 as a photocatalyst [115, 116]. Aromatic and aliphatic nitro compounds were reduced to corresponding amines using TiO2 as catalyst [117]. The direct reduction of imines to corresponding secondary amines was studied [118].
The sunlight reaching the earth’s surface contains UV, visible and infrared wavelength. The Sun releases ultraviolet (UV) radiation in three different wavelengths, and all are harmful in different ways. These wavelengths in sunlight are called UVA (315–400 nm), UVB (280–315 nm) and UVC (100–280 nm) [119]. Because the earth’s atmosphere blocks most UVC rays, UVC does not generally reach the earth’s surface to a significant degree. Therefore, they are not thought to be important contributors to the biological effects on human skin [120]. UVA wavelength penetrates more deeply into the skin causing photo-aging and the formation of skin cancer. UVB is shorter, and damages the surface of the skin. The damage from UVB can cause sunburn and cancer [121, 122, 123, 124].
TiO2 is a semiconducting material with very high refractive index. The high refractive index is what allows the substance to scatter visible light. The current method of preventive treatment again harmful UV radiation involves suspending a substance that either absorbs or scatters UV radiation in a thick emulsion, called sunscreen. Titanium dioxide (TiO2) is an ingredient in sunscreens where its loading is frequently 2–15%. Sunscreen typically contain chemical filters that are organic compounds that absorb strongly the UV (most often UVB) and physical filters such as TiO2 and ZnO that block UVA and UVB sunlight through absorption, reflection and scattering.
In biomedicine, TiO2 nanoparticles with their extraordinary stability, exceptional photo-reactivity, and biocompatibility have a special place in biomedical solutions. The therapeutic potential of TiO2 lies in the ability of these particles in response to light to produce reactive oxygen species (ROS). Production of ROS is the main factor in causing detrimental effects on cells. This effect was first applied by Cai
In both photodynamic therapy (PDT) and sonodynamic therapy (SDT), nano-structured titanium dioxide is used as an agent to produce reactive oxygen species (ROS). Photodynamic therapy (PDT) is an anti-tumor method in which photosensitive agent is applied and target area is illuminated for the activation of the agent. TiO2 is normally a photocatalyst that produces oxidizing radicals by reacting with water during UV exposure and can damage nearby cells [127, 128]. Titanium dioxide and zinc oxide are two of the most effective photosensitizers for PDT applications. In sonodynamic therapy, TiO2 acts as a sonocatalyst. Studies have shown that TiO2 particles can promote the production of hydroxyl (OH•) radicals by ultrasound irradiation even in dark conditions [129, 130]. Ultrasound technology has been already used for some cancer therapies, either by generating localized heating using high intensity ultrasound or by activating a drug release using low intensity ultrasound. Ultrasound can penetrate inches below the skin. Therefore, it can be used to activate TiO2 nanoparticles deep below the skin surface.
TiO2 has been considered to be a good material for the design of drug carriers, for the reasons that the shape and size of TiO2 nanoparticles can be engineered to control their electronic and chemical properties, and the surface of TiO2 nanoparticles can be functionalized with various drug molecules [131, 132]. These capabilities bring new opportunities for more efficient site-selective chemistry of TiO2, and form the vehicles for drug delivery applications.
Titanium dioxide is a stable, non-toxic inorganic material with very high refractive index, and can scatter visible light almost completely. The particle sizes for pigmentary TiO2 are generally engineered to be around 250 nm to have optimized light scattering property. After coating with inorganic compounds such as alumina or silica, the catalytic activity on the surface of TiO2 particles is suppressed and the weather resistance is improved. Because of the superior optical properties and chemical stability, TiO2 has been developed and used as white pigment over several decades. Pigmentary titanium dioxide has excellent ability to impart brightness and opacity. Titanium dioxide has now been a well established inorganic white pigment and is widely applied in the coatings, plastics, paper manufacturing, and in many common products. Global sales of titanium dioxide pigment were about 6 million tons in 2017 and the growth trend of global titanium dioxide pigment sales is continuing over the recent years.
Titanium dioxide is also a semiconducting material which is characterized by a filled valence band and an empty conduction band. When excited by photons which have energy equal to or higher than their band gap, electrons (e−) in valence band of TiO2 are promoted to the conduction band and holes (h+) are created in the valence band of TiO2. Because of the discovery of photocatalytic properties of titanium dioxide, and the ability to engineer TiO2 nanomaterials for controlling their electronic and chemical properties, the applications of titanium dioxide as functional materials have become the focus of enormous research and development in the recent years. The applications of nano-structured TiO2 can now be found in a wide range of areas including electronic materials, energy, environment, health & medicine, and catalysts. A number of materials containing nano-structured TiO2 have become the important commercial products. Further research is continuing to modify the electronic and chemical properties, as well as surface characteristics of TiO2 for the creation of more efficient TiO2 functional materials in more specific application areas.
This work was financially supported by the Pangang Group under a Basic Research Grant.
The authors declare no conflict of interest.
Emotions are essential in our daily lives, with an enormous repercussion on perception, cognition, learning and rational decision-making processes [1]. As a result, the affective neuroscience has emerged with the purpose of studying the influence of emotions on areas like psychology, philosophy or neurobiology, among many others [1]. The emotional states defined in the literature range from a few basic emotions [2] to several complex emotions created as combinations of the basics [3]. These emotional states can be classified according to different models, being the circumplex model of Russell one of the most widely used [4]. This bidimensional model distributes all the existing emotional states according to two emotional parameters, namely valence and arousal. Valence represents the degree of pleasantness or unpleasantness produced by an emotional stimulus, whereas arousal measures the activation or deactivation that a stimulus provokes. The location of each emotional state in the circumplex model is determined by its level of both dimensions, as shown in Figure 1.
Circumplex model of Russell for classification of emotions based on their level of valence and arousal.
Emotions also play a key role in human communication and interaction processes. Nevertheless, the human-machine interfaces (HMIs) are still not able to identify human emotional states. In a digital society in which those systems are daily introduced in multiple ordinary scenarios, it becomes crucial to supplement this lack of emotional intelligence of HMIs. In this sense, the aim of the Affective Computing science is to endow those systems with the capability to automatically detect and interpret human emotions and decide which actions to execute accordingly, thus improving the interactions between people and machines [5, 6].
The detection of emotional states can be conducted by means of the assessment of bodily reactions to emotional stimuli, for which different physiological variables can be measured and analyzed. One of the most widely studied in the last years is the electroencephalography (EEG), which represents the electrical activity generated in the brain due to neural connections [7]. The selection of EEG recordings instead of other physiological signals is justified by the fact that the brain generates the first impulse against any stimulus, and then it is spread to the rest of peripheral systems through the central nervous system. In this sense, EEG signals represent the activity of the source of the emotional response, whereas the rest of physiological variables can be considered as secondary effects of the brain’s performance [8]. As a consequence, the number of works focused on the analysis of EEG time series for emotions detection has notably increased in the last years [9].
The evaluation of EEG recordings has been traditionally conducted from a linear perspective, especially in the frequency domain, studying features such as the spectral power or the asymmetry between the two brain hemispheres in different frequency bands [10]. However, the brain activity is far from being considered linear. Contrarily, neural processes follow a completely heterogeneous and nonstationary performance even at both cellular and global level [11]. With this respect, the application of linear algorithms may not report a complete description of the brain’s behavior [12]. For this reason, nonlinear methodologies have been widely applied for discovering underlying information unrevealed by traditional linear techniques [13]. Indeed, nonlinear indices have already outperformed the results derived from the application of those linear algorithms for the evaluation of various mental processes, including the recognition of emotions [13].
Among the different nonlinear methodologies that can be found in the literature, entropy indices have been widely applied in the context of emotions recognition with EEG recordings [14]. Entropy represents the rate of information reported by a time series, describing the nonlinear characteristics of a nonstationary system [15]. Hence, entropy metrics become promising tools for the assessment of the chaotic dynamics of a nonstationary system such as the brain. Indeed, in the literature many studies have applied these nonlinear methodologies for the identification of emotional states from EEG recordings. The present manuscript summarizes the main discoverings of the last years in the scientific field of emotions recognition from EEG signals with entropy indices.
Entropy was firstly defined in thermodynamics, referring to the distribution probability of molecules in a fluid system [15]. In information theory and signals analysis, this concept was adapted by Shannon, who defined entropy as a measure of the information provided by a time series, describing its complexity, irregularity or unpredictability [16]. With respect to the EEG analysis, many entropy indices have been introduced and successfully applied for the study of various physical and mental disorders, like epilepsy [17], Alzheimer [18], autism [19] or depression [20], among others. As a result of the valuable outcomes, entropy metrics have also been introduced in the research field of emotions recognition from EEG recordings [14]. The following subsections give a brief mathematical description of the entropy metrics mainly applied for emotions detection.
The irregularity of a signal represents the rate of repetitiveness of patterns, reaching higher values for non-repetitive and disordered time series, and lower for sequences with a high rate of occurrence [21]. One of the regularity-based entropy metrics widely used is the approximate entropy (ApEn), which evaluates the probability of having repetitive patterns and assigns a non-negative number to each sequence in terms of its repetitiveness, with lower values for more recurrent patterns [22]. Mathematically, ApEn is computed as
where
Moreover, quadratic sample entropy (QSampEn) emerged as an improvement of SampEn to make it insensitive to the value of the threshold
The predictability of a nonstationary system is related to its stable and deterministic evolution in time. Most of the entropy metrics for predictability measurement are symbolic indices that convert the original signal into a sequence of discrete symbols to form sequences [25]. After this symbolization, the evaluation of the predictability of a time series can be carried out with multiple techniques. The most commonly used is the Shannon entropy (ShEn), which quantifies the predictability of a signal in terms of the probability distribution of its amplitudes [16]. The mathematical expression of ShEn is
being
The Rényi entropy (REn) is a generalization of ShEn that is also widely used for the quantification of underlying dynamics in symbolized signals [26]. Concretely, REn provides a better characterization of some rare and frequent ordinal sequences, and it is defined as
being
The version of ShEn for continuous random variables, called differential entropy (DEn), has received growing interest in the last years [27]. This entropy index can be expressed as
where
As EEG signals follow a Gaussian distribution after the application of a band-pass filtering approach, the DEn of each sub-frequency band previously obtained by a Fast Fourier transform can be obtained according to the aforementioned equation [27].
Another widely used predictability-based entropy metric is permutation entropy (PerEn), which is a fast and insensitive to noise metric that evaluates the order of the symbols within a pattern [28]. Briefly, the original time series is symbolized to obtain ordinal sequences
One of the limitations of PerEn is that it only considers the order of the symbols in a pattern, without taking into account their amplitudes. This limitation has been recently solved by means of the introduction of amplitude-aware permutation entropy (AAPE) [29]. This improvement of PerEn computes the probability
Another option for the assessment of predictability of a time series is the spectral entropy (SpEn) [30]. In this case, the spectral power of a determined frequency is computed and normalized with respect to the total power, which gives a probability density function
The time series generated by nonlinear and nonstationary systems like the brain usually present highly complex dynamics derived from different simultaneous mechanisms that operate in multiple time scales [31]. As a result, the brain behavior cannot be completely described by means of single-scale methods. Therefore, multiscale variations of the aforementioned entropy metrics have been introduced with the purpose of revealing undiscovered information related to the multiscale nature of the EEG recordings. For the computation of multiscale entropy (MSE), the original signal
Therefore, all the previously defined entropy indices can be computed in a multiscale form for the coarse-grained series as defined above.
Another multiscale option is the wavelet entropy (WEn), which makes use of the decomposition of the original signal in different scales by means of the wavelet transform [32]. After the decomposition of the time series, the probability distribution
On the other hand, the characteristics of the autocorrelation function of some signals require the consideration of a lag or time delay
Finally, PerMin is obtained in the limit
In the literature, there are various studies that have applied regularity-based entropy measures for emotions detection with EEG recordings. A brief summary of those works is presented in Table 1, with information about the year of publication, the experimental design (including the number of emotions detected, subjects, EEG channels and type of stimulus), the features extracted, the classification models implemented, and the results obtained in each case. As can be observed, the interest in these metrics started growing in 2014, especially computing ApEn and SampEn for the detection of a number of emotions ranging between 2 and 4. In many cases, the signals analyzed were extracted from the publicly available Database for Emotion Analysis using Physiological Signals (DEAP), which consisted on a total of 32 healthy subjects watching emotional videoclips during the registration of their EEG with 32 channels [47]. Hence, different studies tested their methods on the same EEG recordings, thus allowing a direct comparison of the results obtained [37, 38, 39, 41, 42, 48]. The rest of works had different experimental designs. In terms of the classification models, support vector machines (SVM) were selected in most of the cases [35, 37, 38, 40, 43, 44, 45]. The outcomes derived from these studies presented a classification accuracy (Acc) ranging between 73% and 95%, being the frontal and parietal brain regions the most relevant for the detection of emotional states with these regularity-based entropies.
Ref. (Year) | Experimental design | Features | Classifier | Results |
---|---|---|---|---|
[35] (2011) | 2 emotions, 15 subjects, 5 EEG channels, images | ApEn + others | SVM1 | Acc = 73.25% |
[36] (2014) | Depression, 60 subjects, 24 EEG channels, eyes open/closed, no stimuli | ApEn + others | — | Higher irregularity for healthy than for depressed |
[37] (2014) | 4 emotions, DEAP2 | SampEn | SVM | Acc = 80.43% |
[38] (2016) | 4 emotions, DEAP | SampEn + others | SVM | Acc = 94.98% (two classes) and 93.20% (four classes) |
[39] (2016) | 2 emotions, DEAP | SampEn, QSampEn + others | DT3 | Acc = 75.29% |
[40] (2017) | 3 emotions, 44 subjects, 31 EEG channels, images | ApEn + others | SVM | Acc = 75.5% |
[41] (2018) | Valence and arousal, DEAP | SampEn + others | MLP4, DST5 | Acc = 87.43% (arousal) and 88.74% (valence) |
[42] (2018) | 4 emotions, DEAP | SampEn + others | PSAE6 | Acc = 93.6% |
[43] (2018) | 4 emotions, 10 subjects, 14 EEG channels, film clips | ApEn + others | SVM, DBN7 | Acc = 87.32% |
[44] (2019) | 4 emotions, 8 subjects, 12 EEG channels, music | ApEn, SampEn + others | SVM, C4.5, LDA8 | Acc = 84.91% (valence) and 89.65% (arousal) |
[45] (2020) | 3 emotions, SEED9 | DySampEn10 | SVM | Acc = 84.67% |
[46] (2021) | 2 emotions, DEAP | CSampEn11 | — | More coordination in parietal and occipital |
Studies of emotions recognition from EEG recordings with regularity-based entropy indices.
SVM: Support vector machine.
DEAP: Database for emotion analysis using physiological signals.
DT: Decision tree.
MLP: Multi-layer perceptron.
DST: Dempster-Shafer theory.
PSAE: Parallel stacked autoencoders.
DBN: Deep belief networks.
LDA: Linear discriminant analysis.
SEED: SJTU emotion EEG database.
DySampEn: Dynamic SampEn.
CSampEn: Cross-sample entropy.
On the other hand, the predictability-based entropy indices have been the most applied for the assessment of different emotional states from EEG recordings. The main studies that have used these entropy metrics for that purpose are included in Tables 2 and 3. It can be observed that only a few works studied these indices between 2011 and 2015 [49, 50, 51, 52, 63]. Nevertheless, the interest in these predictability measures has notably increased since 2017 until nowadays. More precisely, DEn is the predictability-based metric that has gained a considerable visibility since 2018, thus Table 3 only includes studies based on the application of DEn. The rest of predictability-based entropy metrics, i.e. ShEn, REn, SpEn, and permutation indices, are contained in Table 2. It is interesting to note that the majority of these works in Table 2 analyzed the EEG signals contained in the DEAP database, whereas only a few tested those metrics with different experiments. As for the regularity-based indices, the number of emotions identified in works in Table 2 ranged from 2 to 4, and only one study recognized 5 emotional states [49]. In terms of the classifiers implemented, SVM approaches were preferred over other models in the majority of the studies. The results obtained presented inconsistent Acc values, ranging from 65–99%, being the frontal and parietal/occipital lobes the most relevant in emotional processes. With respect to the studies in Table 3, it can be noticed that the majority of them followed the same experimental procedure, but in this case, another public dataset different from DEAP was selected. Indeed, recordings from the SJTU Emotion EEG Dataset (SEED) were chosen and assessed for the detection of three emotional states, namely positive, neutral and negative [63]. This database contained EEG recordings with 62 channels from 15 subjects during the visualization of film clips with emotional content [63]. The selection of the classification approaches was quite inconsistent across the different studies. However, it can be observed that deep learning approaches like convolutional neural networks (CNN) have been progressively introduced in the literature for their application in emotion recognition researches. The accuracy results reported in these works were between 68% and 99%. Furthermore, some of them demonstrated that DEn was more suitable than some linear metrics for the identification of emotions [63, 68].
Ref. (Year) | Experimental design | Features | Classifier | Results |
---|---|---|---|---|
[49] (2011) | 5 emotions, 20 subjects, 24 and 62 EEG channels, videos | ShEn, SP1 | LDA, KNN2 | Entropy better than linear. Acc = 83.04% with 62 channels |
[50] (2013) | Stress, 13 subjects, 3 EEG channels, eyes closed, no stimuli | REn + others | ANOVA3 | Lower complexity in stress than in calmness |
[51] (2015) | 4 emotions, 8 subjects, 20 EEG channels, audiovisual stimuli | ShEn, REn | MC-LSSVM4 | Acc = 84.79% |
[52] (2015) | 4 emotions, 25 subjects, 3 EEG channels, music | SpEn | SVM, KNN, CFNN5 | Acc = 93.66% (valence) and 93.29% (arousal) |
[53] (2017) | 2 emotions, DEAP | PerEn, AAPE, QSampEn | SVM | Acc = 81.31% |
[54] (2017) | Valence and arousal, DEAP | SpEn, ShEn | LSSVM6, D-RFE7 | Acc = 78.96% (arousal) and 71.43% (valence) |
[55] (2017) | 4 emotions, DEAP | SpEn, ShEn + others | Three-stage decision method | Acc = 86.67% |
[56] (2018) | Arousal and valence, DEAP | SpEn, spectral and statistics | SVM, KNN, NB8 | Spectral and statistics better than SpEn |
[57] (2018) | 2, 3, 4 and 5 emotions, DEAP | REn + others | SVM | Acc = 73.8–86.2% |
[58] (2018) | Depression, 213 subjects, 3 EEG channels, sounds | ShEn, SpEn + others | KNN | Acc = 79.27% |
[59] (2019) | 4 emotions, DEAP | ShEn, SpEn + others | LSSVM | Acc = 65.13% |
[60] (2019) | 4 emotions, DEAP | ShEn, PerEn + others | SVM | Best results with PerEn |
[61] (2020) | 2 emotions, DEAP | CEn9, QSampEn | SVM | Acc = 80.31% |
[62] (2020) | 4 emotions, DEAP | SpEn, ShEn + others | SVM, NB, ANN10 | Acc = 98.7% with ANN |
[48] (2021) | 4 emotions, DEAP | AAPE, PerMin + others | SVM | Acc = 96.39% |
Studies of emotions recognition from EEG recordings with predictability-based entropy indices (ShEn, SpEn, REn, PerEn, AAPE.
SP: Spectral power.
KNN: K-nearest neightbor.
ANOVA: Analysis of variance.
MC-LSSVM: Multiclass least-square support vector machine.
CFNN: Cascade-forward neural network.
LSSVM: Least-square support vector machine.
D-RFE: Dynamical recursive feature elimination.
NB: Naive Bayes.
CEn: Conditional entropy.
ANN: Artificial neural network.
Ref. (Year) | Experimental design | Features | Classifier | Results |
---|---|---|---|---|
[63] (2015) | 3 emotions, SEED | DEn, SP, statistics | DBN | DEn better than SP and statistics. Acc = 85% |
[64] (2018) | 4 emotions, two experiments: DEAP and SEED | DEn + others | GELM1 | Acc = 69.67% (DEAP) and 91.07% (SEED) |
[65] (2018) | 3 emotions, SEED | DEn | HCNN2 | Best results in |
[66] (2018) | 3 emotions, 14 subjects, 64 EEG channels, film clips | DEn + others | GRSLR3 | Acc = 80.27% |
[67] (2018) | 3 emotions, two experiments: DEAP and SEED | DEn | — | Best results with SEED database |
[68] (2018) | 3 emotions, SEED | DEn, SP, statistics | DGCNN4 | DEn better than SP and statistics. Acc = 90% |
[69] (2019) | 3 emotions, SEED | DEn | LDA | Acc = 68% |
[70] (2019) | 2 emotions in patients with disorder of conciousness, 18 subjects, 32 EEG channels, videos | DEn | SVM | Acc = 91.5% |
[71] (2019) | 3 emotions, SEED | DEn | STNN5 | Acc = 84.16% |
[72] (2019) | 3 emotions, SEED | DEn | LR6 | Acc = 86% |
[73] (2020) | 3 emotions, SEED | DEn | MTL7 | Acc = 88.92% |
[74] (2020) | 3 emotions, SEED | DEn | CNN | Acc = 90.63% |
[75] (2020) | 3 emotions, SEED | DEn | CNN | Acc = 90.41% |
[76] (2020) | High-low valence and arousal, DEAP | DEn + others | LORSAL8 | Acc = 77.17% |
[77] (2020) | 3 emotions, SEED | DEn + others | CNN | Acc = 99.7% |
[78] (2020) | 3 emotions, SEED | DEn + others | SRU9 | Acc = 83.13% |
[79] (2021) | Valence and arousal, DEAP | DEn | CNN | Acc = 90.45% (valence) and 90.6% (arousal) |
Studies of emotions recognition from EEG recordings with predictability-based entropy indices (DEn).
GELM: Graph-regularized extreme learning machine.
HCNN: Hierarchical convolutional neural network.
GRSLR: Graph regularized sparse linear regression.
DGCNN: Dynamical graph convolutional neural network.
STNN: Spatial–temporal neural network.
LR: Linear regression.
MTL: Multisource transfer learning.
LORSAL: Logistic regression via variable splitting and augmented Lagragian.
SRU: Simple recurrent unit network.
Finally, Table 4 shows the main works focused on the application of multiscale and multilag entropy approaches for detecting emotions with EEG recordings. As can be observed, the study of these indices has emerged in the last few years, especially since 2019 until nowadays. In these works, the number of emotional states studied ranged from 2 to 5, which is in line with the rest of entropy metrics evaluated. Furthermore, the signals from the DEAP database were also chosen by some of the studies included in the table. It can be noticed that the selection of the classification models was slightly inconsistent among the different studies, although SVM and deep learning approaches were the most selected. As in the previous cases, the final outcomes obtained presented accuracy values with a high variability, ranging from 73–98%.
Ref. (Year) | Experimental design | Features | Classifier | Results |
---|---|---|---|---|
[80] (2016) | 5 emotions, 30 subjects, 6 EEG channels, video clips | MMSampEn1 | — | Higher irregularity for higher arousal levels |
[81] (2019) | 2 emotions, DEAP | CMQSampEn2, CMAAPE3 | SVM, DT | Acc = 86.35% |
[82] (2019) | 4 emotions, DEAP | WEn | SVM, FCM4 | Acc = 73.32% |
[83] (2019) | 2 emotions, DEAP | PerMin, DPerEn5 | KNN | Acc = 92.32% |
[84] (2020) | 3 emotions, 10 subjects, 14 EEG channels, video clips | WEn | ANN | Acc = 98% |
[85] (2021) | 3 emotions, SEED | MSpEn6 + others | ARF7 | Acc = 94.4% |
[86] (2021) | Enjoyment, 28 subjects, 8 EEG channels, art pieces | MSampEn | SVM, RVM8 | Acc = 91.18% |
Studies of emotions recognition from EEG recordings with multiscale and multilag entropy indices.
MMSampEn: Multivariate-multiscale SampEn.
CMQSampEn: Composite multiscale QSampEn.
CMAAPE: Composite multiscale AAPE.
FCM: Fuzzy cognitive map.
DPerEn: Delayed PerEn.
MSpEn: Multiscale SpEn.
ARF: Autoencoder based random forest.
RVM: Relevance vector machine.
The application of entropy metrics for the recognition of emotions from EEG signals has received increasing attention in the last years, reporting valuable insights about the brain’s performance under different emotional conditions. However, the high variability of the results obtained could be justified by various aspects. On the one hand, the experimentation is different for each study, since there are no gold standards of experimental procedures. In this sense, the number of participants and their gender, age, or cultural characteristics, are very incosistent among studies, thus the results may not be representative of the whole population. In addition, the type of stimulus used for emotions elicitation (images, sounds, videos, etc.) is also inconsistent, since there is no consensus about which is the optimum option for triggering a strong emotional response [87]. The duration of the stimulus is another unclear point, thus different criteria are followed by each research group. Finally, although the locations of EEG electrodes are standardized, the number of EEG channels recorded is different in each experiment, ranging from 3 to 64. Moreover, some works assessed the signals corresponding to only one brain area, thus discarding the information that could be reported by the rest of regions.
All those experimental differences could bias the possibility of obtaining universal results that could be generalized to the whole population. As a consequence of all those discrepancies between experimental procedures, the studies presented in this manuscript should be carefully interpreted and compared. In addition, as not all the publications give a thorough description of their methodology, their experiments could not be reproduced by other research groups. Therefore, the assessment of signals extracted from publicly available databases, like DEAP or SEED, could eliminate this limitation, since the experimental procedure would be the same for different authors. In this sense, the reproducibility and comparability of the results obtained would be guaranteed, and the differences in the outcomes would directly appear due to the diversity of analysis methods and classification approaches.
The variability of the results could also be a consequence of the intrinsic differences of the entropy metrics evaluated. Indeed, regularity-based, predictability-based, and multiscale/multilag approaches evaluate the complexity of time series from different perspectives. Therefore, the application of either one or other type of entropy index on the same problem could report completely divergent outcomes. Nevertheless, instead of considering these inequalities as contradictory, it should be regarded as a sign of complementarity between the different entropy metrics. For instance, some characteristics of a nonlinear signal could be properly assessed with regularity-based entropies, and other dynamics would be better described by predictability and symbolic entropy measures. Consequently, the selection of either one or other type of entropy index should be done taking into account the information that is wanted to be extracted from a nonstationary time series, also considering that the combination of different entropies would report a more complete description of the nonlinear processes.
The promising outcomes presented in these studies make the entropy metrics a useful tool for the recognition of emotions from EEG recordings. However, the majority of the works are mainly focused on obtaining great classification accuracy values, for which advanced classification models with hundreds of input features are implemented. Despite providing notable numerical results in many cases, the combination of such a large amount of data in complex classification schemes derives in a total loss of clinical interpretation of the results. In this regard, information about which are the most relevant brain regions, or which EEG channels do a higher contribution to the classification model, cannot be obtained. Thus, it becomes impossible to make a thorough analysis of the brain’s behavior under the emotional states detected. As a result, it would be interesting to modify some methodological aspects in this kind of studies in order to ensure the clinical interpretation of the results and reveal new insights about mental processes under emotional conditions.
Given the nonlinear and nonstationary nature of the brain, entropy indices are suitable tools for a complete description of the brain dynamics in different scenarios, including the recognition of emotional states. This chapter summarizes the main recent contributions to the research field of emotions detection through the application of entropy indices for the analysis of EEG recordings. In this sense, regularity-based, predictability-based, and multiscale/multilag entropy approaches have demonstrated their capability to discern between different emotional states and discover new insights about the brain dynamics in emotional processes. Taking into account the valuable results obtained in the studies presented in this chapter, entropy metrics could become one of the first options to be considered in systems for automatic emotions identification from EEG signals.
This work was partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI) /European Regional Development Fund (FEDER, UE) under EQC2019-006063-P, PID2020-115220RB-C21, and 2018/11744 grants, and by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz García-Martínez holds FPU16/03740 scholarship from Spanish Ministerio de Educación y Formación Profesional.
The authors declare no conflict of interest.
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His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNVJQA4/Profile_Picture_2022-03-07T13:23:04.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. His research interests include biochemistry, oxidative stress, reactive species, antioxidants, lipid peroxidation, inflammation, reproductive hormones, phenolic compounds, female infertility.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Associate Prof.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/15648_n.jpg",biography:"Dr. Mohd Aftab Siddiqui is currently working as Assistant Professor in the Faculty of Pharmacy, Integral University, Lucknow for the last 6 years. He has completed his Doctor in Philosophy (Pharmacology) in 2020 from Integral University, Lucknow. He completed his Bachelor in Pharmacy in 2013 and Master in Pharmacy (Pharmacology) in 2015 from Integral University, Lucknow. He is the gold medalist in Bachelor and Master degree. He qualified GPAT -2013, GPAT -2014, and GPAT 2015. His area of research is Pharmacological screening of herbal drugs/ natural products in liver and cardiac diseases. He has guided many M. Pharm. research projects. He has many national and international publications.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. degree from Integral University. Currently, he’s working as an Assistant Professor of Pharmaceutics in the Faculty of Pharmacy, Integral University. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than 32 original articles published in reputed journals, 3 edited books, 5 book chapters, and a number of scientific articles published in ‘Ingredients South Asia Magazine’ and ‘QualPharma Magazine’. He is a member of the American Association for Cancer Research, International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs that aim to provide practical solutions to current healthcare problems.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. Currently, he is a full professor at Central South University and Shandong First Medical University, and an advisor to MS/PhD students and postdoctoral fellows. He is also a fellow of the Royal Society of Medicine and European Association for Predictive Preventive Personalized Medicine (EPMA), a national representative of EPMA, and a member of the American Society of Clinical Oncology (ASCO) and the American Association for the Advancement of Sciences (AAAS). He is also the editor in chief of International Journal of Chronic Diseases & Therapy, an associate editor of EPMA Journal, Frontiers in Endocrinology, and BMC Medical Genomics, and a guest editor of Mass Spectrometry Reviews, Frontiers in Endocrinology, EPMA Journal, and Oxidative Medicine and Cellular Longevity. He has published more than 148 articles, 28 book chapters, 6 books, and 2 US patents in the field of clinical proteomics and biomarkers.",institutionString:"Shandong First Medical University",institution:{name:"Affiliated Hospital of Shandong Academy of Medical Sciences",country:{name:"China"}}},{id:"297507",title:"Dr.",name:"Charles",middleName:"Elias",surname:"Assmann",slug:"charles-assmann",fullName:"Charles Assmann",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/297507/images/system/297507.jpg",biography:"Charles Elias Assmann is a biologist from Federal University of Santa Maria (UFSM, Brazil), who spent some time abroad at the Ludwig-Maximilians-Universität München (LMU, Germany). He has Masters Degree in Biochemistry (UFSM), and is currently a PhD student at Biochemistry at the Department of Biochemistry and Molecular Biology of the UFSM. His areas of expertise include: Biochemistry, Molecular Biology, Enzymology, Genetics and Toxicology. He is currently working on the following subjects: Aluminium toxicity, Neuroinflammation, Oxidative stress and Purinergic system. Since 2011 he has presented more than 80 abstracts in scientific proceedings of national and international meetings. Since 2014, he has published more than 20 peer reviewed papers (including 4 reviews, 3 in Portuguese) and 2 book chapters. He has also been a reviewer of international journals and ad hoc reviewer of scientific committees from Brazilian Universities.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",country:{name:"Brazil"}}},{id:"217850",title:"Dr.",name:"Margarete Dulce",middleName:null,surname:"Bagatini",slug:"margarete-dulce-bagatini",fullName:"Margarete Dulce Bagatini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217850/images/system/217850.jpeg",biography:"Dr. Margarete Dulce Bagatini is an associate professor at the Federal University of Fronteira Sul/Brazil. She has a degree in Pharmacy and a PhD in Biological Sciences: Toxicological Biochemistry. She is a member of the UFFS Research Advisory Committee\nand a member of the Biovitta Research Institute. She is currently:\nthe leader of the research group: Biological and Clinical Studies\nin Human Pathologies, professor of postgraduate program in\nBiochemistry at UFSC and postgraduate program in Science and Food Technology at\nUFFS. She has experience in the area of pharmacy and clinical analysis, acting mainly\non the following topics: oxidative stress, the purinergic system and human pathologies, being a reviewer of several international journals and books.",institutionString:"Universidade Federal da Fronteira Sul",institution:{name:"Universidade Federal da Fronteira Sul",country:{name:"Brazil"}}},{id:"226275",title:"Ph.D.",name:"Metin",middleName:null,surname:"Budak",slug:"metin-budak",fullName:"Metin Budak",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226275/images/system/226275.jfif",biography:"Metin Budak, MSc, PhD is an Assistant Professor at Trakya University, Faculty of Medicine. He has been Head of the Molecular Research Lab at Prof. Mirko Tos Ear and Hearing Research Center since 2018. His specializations are biophysics, epigenetics, genetics, and methylation mechanisms. He has published around 25 peer-reviewed papers, 2 book chapters, and 28 abstracts. He is a member of the Clinical Research Ethics Committee and Quantification and Consideration Committee of Medicine Faculty. His research area is the role of methylation during gene transcription, chromatin packages DNA within the cell and DNA repair, replication, recombination, and gene transcription. His research focuses on how the cell overcomes chromatin structure and methylation to allow access to the underlying DNA and enable normal cellular function.",institutionString:"Trakya University",institution:{name:"Trakya University",country:{name:"Turkey"}}},{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",slug:"anca-pantea-stoian",fullName:"Anca Pantea Stoian",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",biography:"Anca Pantea Stoian is a specialist in diabetes, nutrition, and metabolic diseases as well as health food hygiene. She also has competency in general ultrasonography.\n\nShe is an associate professor in the Diabetes, Nutrition and Metabolic Diseases Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania. She has been chief of the Hygiene Department, Faculty of Dentistry, at the same university since 2019. Her interests include micro and macrovascular complications in diabetes and new therapies. Her research activities focus on nutritional intervention in chronic pathology, as well as cardio-renal-metabolic risk assessment, and diabetes in cancer. She is currently engaged in developing new therapies and technological tools for screening, prevention, and patient education in diabetes. \n\nShe is a member of the European Association for the Study of Diabetes, Cardiometabolic Academy, CEDA, Romanian Society of Diabetes, Nutrition and Metabolic Diseases, Romanian Diabetes Federation, and Association for Renal Metabolic and Nutrition studies. She has authored or co-authored 160 papers in national and international peer-reviewed journals.",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",country:{name:"Romania"}}},{id:"279792",title:"Dr.",name:"João",middleName:null,surname:"Cotas",slug:"joao-cotas",fullName:"João Cotas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/279792/images/system/279792.jpg",biography:"Graduate and master in Biology from the University of Coimbra.\n\nI am a research fellow at the Macroalgae Laboratory Unit, in the MARE-UC – Marine and Environmental Sciences Centre of the University of Coimbra. My principal function is the collection, extraction and purification of macroalgae compounds, chemical and bioactive characterization of the compounds and algae extracts and development of new methodologies in marine biotechnology area. \nI am associated in two projects: one consists on discovery of natural compounds for oncobiology. The other project is the about the natural compounds/products for agricultural area.\n\nPublications:\nCotas, J.; Figueirinha, A.; Pereira, L.; Batista, T. 2018. An analysis of the effects of salinity on Fucus ceranoides (Ochrophyta, Phaeophyceae), in the Mondego River (Portugal). Journal of Oceanology and Limnology. in press. DOI: 10.1007/s00343-019-8111-3",institutionString:"Faculty of Sciences and Technology of University of Coimbra",institution:null},{id:"279788",title:"Dr.",name:"Leonel",middleName:null,surname:"Pereira",slug:"leonel-pereira",fullName:"Leonel Pereira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/279788/images/system/279788.jpg",biography:"Leonel Pereira has an undergraduate degree in Biology, a Ph.D. in Biology (specialty in Cell Biology), and a Habilitation degree in Biosciences (specialization in Biotechnology) from the Faculty of Science and Technology, University of Coimbra, Portugal, where he is currently a professor. In addition to teaching at this university, he is an integrated researcher at the Marine and Environmental Sciences Center (MARE), Portugal. His interests include marine biodiversity (algae), marine biotechnology (algae bioactive compounds), and marine ecology (environmental assessment). Since 2008, he has been the author and editor of the electronic publication MACOI – Portuguese Seaweeds Website (www.seaweeds.uc.pt). He is also a member of the editorial boards of several scientific journals. Dr. Pereira has edited or authored more than 20 books, 100 journal articles, and 45 book chapters. He has given more than 100 lectures and oral communications at various national and international scientific events. He is the coordinator of several national and international research projects. In 1998, he received the Francisco de Holanda Award (Honorable Mention) and, more recently, the Mar Rei D. Carlos award (18th edition). He is also a winner of the 2016 CHOICE Award for an outstanding academic title for his book Edible Seaweeds of the World. In 2020, Dr. Pereira received an Honorable Mention for the Impact of International Publications from the Web of Science",institutionString:"University of Coimbra",institution:{name:"University of Coimbra",country:{name:"Portugal"}}},{id:"61946",title:"Dr.",name:"Carol",middleName:null,surname:"Bernstein",slug:"carol-bernstein",fullName:"Carol Bernstein",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/61946/images/system/61946.jpg",biography:"Carol Bernstein received her PhD in Genetics from the University of California (Davis). She was a faculty member at the University of Arizona College of Medicine for 43 years, retiring in 2011. Her research interests focus on DNA damage and its underlying role in sex, aging and in the early steps of initiation and progression to cancer. In her research, she had used organisms including bacteriophage T4, Neurospora crassa, Schizosaccharomyces pombe and mice, as well as human cells and tissues. She authored or co-authored more than 140 scientific publications, including articles in major peer reviewed journals, book chapters, invited reviews and one book.",institutionString:"University of Arizona",institution:{name:"University of Arizona",country:{name:"United States of America"}}},{id:"182258",title:"Dr.",name:"Ademar",middleName:"Pereira",surname:"Serra",slug:"ademar-serra",fullName:"Ademar Serra",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/182258/images/system/182258.jpeg",biography:"Dr. Serra studied Agronomy on Universidade Federal de Mato Grosso do Sul (UFMS) (2005). He received master degree in Agronomy, Crop Science (Soil fertility and plant nutrition) (2007) by Universidade Federal da Grande Dourados (UFGD), and PhD in agronomy (Soil fertility and plant nutrition) (2011) from Universidade Federal da Grande Dourados / Escola Superior de Agricultura Luiz de Queiroz (UFGD/ESALQ-USP). Dr. Serra is currently working at Brazilian Agricultural Research Corporation (EMBRAPA). His research focus is on mineral nutrition of plants, crop science and soil science. Dr. Serra\\'s current projects are soil organic matter, soil phosphorus fractions, compositional nutrient diagnosis (CND) and isometric log ratio (ilr) transformation in compositional data analysis.",institutionString:"Brazilian Agricultural Research Corporation",institution:{name:"Brazilian Agricultural Research Corporation",country:{name:"Brazil"}}}]}},subseries:{item:{id:"17",type:"subseries",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11413,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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