Single Trial Classification of Evoked EEG Signals Due to RGB Colors

Eman Alharbi, Saim Rasheed, Seyed Buhari


Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a real time classification model (online) for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD) technique was used at the signal analysis stage. Various feature extraction methods were investigated to find the best and reliable set, including Event-related spectral perturbations (ERSP), Target mean with Feast Fourier Transform (FFT), Wavelet Packet Decomposition (WPD), Auto Regressive model (AR) and EMD residual. A new feature selection method was created based on the peak's time of EEG signal when red and blue colors stimuli are presented. The ERP image was used to find out the peak's time, which was around 300 ms for the red color and around 450 ms for the blue color. The classification was performed using the Support Vector Machine (SVM) classifier, LIBSVM toolbox being used for that purpose. The EMD residual was found to be the most reliable method that gives the highest classification accuracy with an average of 88.5% and with an execution time of only 14 seconds.


Brain Computer Interface (BCI), Electroenphesalography (EEG), Event Related Potintials, component (ERP); Emperical Mode Decomposition (EMD), Event-related spectral perturbations (ERSP), Wavelet Packet Decomposition (WPD), Autoregrissive (AR), Fast Fourier

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