TY - CHAP AU - Abbas Erfanian AU - Farid Oveisi AU - Ali Shadvar ED - Reza Fazel-Rezai Y1 - 2011-02-04 PY - 2011 T1 - Feature Extraction by Mutual Information Based on Minimal-Redundancy-Maximal-Relevance Criterion and Its Application to Classifying EEG Signal for Brain-Computer Interfaces N2 - Brain Computer Interface (BCI) technology provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity, allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. Recently, there has been a great progress in the development of novel paradigms for EEG signal recording, advanced methods for processing them, new applications for BCI systems and complete software and hardware packages used for BCI applications. In this book a few recent advances in these areas are discussed. BT - Recent Advances in Brain-Computer Interface Systems SP - Ch. 3 UR - https://doi.org/10.5772/13935 DO - 10.5772/13935 SN - PB - IntechOpen CY - Rijeka Y2 - 2024-03-29 ER -