Single Trial Visual Evoked Potential Extraction by Negentropy Maximisation of Independent Components

A novel method based on genetic algorithm maximising negentropy function is proposed to perform blind extraction of Visual Evoked Potential (VEP) from background electroencephalogram (EEG) on a single trial basis for use in speller BCI designs. This method is a simpler and rapidly converging alternative to existing independent component analysis (ICA) algorithms that use neural learning algorithms. In the method, binary coded genetic algorithm maximises the negentropy of the extracted signals on a deflationary basis to obtain the inverse of the mixing matrix. To show the validity of the proposed method, the proposed method was applied to both simulated and real EEG (from BCI competition 2003 – data set IIb) signals. The results show significant SNR enhancement of P300 wave in VEP using only as few as three channel (Fz, Pz, Cz) single trial data. Key-Words: Electroencephalogram, Genetic Algorithm, Negentropy, Visual Evoked Potential

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