Cognitive neuroscience and brain imaging technologies advances has enableddevices to interface directly with the human brain. This is possible through use of sensors that capture signals in the brain, corresponding to certain thought forms. Braincomputer Interface’s (BCI) central element, is a translation algorithm converting electrophysiological input from user into output capable of controlling external devices. This study presents a BCI system which pre-processes and extracts features from Electrocorticography (ECoG) signals using Symlet Wavelets. Signals are classified using Support Vector Machine (SVM) with Radial Basis Function (RBF). It is proposed to optimize the C and Gamma parameters of the RBF kernel using Clonal Selection Algorithm (CLONALG) in this study.
Brain Computer Interfaces (BCIs), Electrocorticography (ECoG), Symlet Wavelets, Support Vector Machine (SVM), Radial Basis Function (RBF), Clonal Selection Algorithm (CLONALG)
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