EEG Filtering Optimization for Code–Modulated Chromatic Visual Evoked Potential–Based Brain–Computer Interface
We present visual BCI classification accuracy improved results after application of high– and low–pass filters to an electroencephalogram (EEG) containing code–modulated visual evoked potentials (cVEPs). The cVEP responses are applied for the brain–computer interface (BCI) in four commands paradigm mode. The purpose of this project is to enhance BCI accuracy using only the single trial cVEP response. We also aim at identification of the most discriminable EEG bands suitable for the broadband visual stimuli. We report results from a pilot study optimizing the EEG filtering using infinite impulse response filters in application to feature extraction for a linear support vector machine (SVM) classification method. The goal of the presented study is to develop a faster and more reliable BCI to further enhance the symbiotic relationships between humans and computers.
KeywordsBrain–computer interface ERP EEG classification cVEP
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