Decoding of multichannel EEG activity from the visual cortex in response to pseudorandom binary sequences of visual stimuli

Electroencephalography (EEG) signals have been an attractive choice to build noninvasive brain computer interfaces (BCIs) for nearly three decades. Depending on the stimuli, there are different responses which one could get from EEG signals. One of them is the P300 response which is a visually evoked response that has been widely studied. Steady state visually evoked potential (SSVEP) is the response to an oscillating stimulus with fixed frequency, which is detectable from the visual cortex. However, there exists some work on using an m-sequence with different lags as the control sequence of the flickering stimuli. In this study, we used several m-sequences instead of one with the intent of increasing the number of possible command options in a BCI setting. We also tested two different classifiers to decide between the m-sequences and study the performance of multi channel classifiers versus single channel classifiers. The study is done over two different flickering frequencies, 15 and 30 Hz to investigate the effect of flickering frequency on the accuracy of the classification methods. Our study shows that the EEG channels are correlated, and although all the channels contain some useful information, but combining them with a multi channel classifier based on the assumption of having conditional independence will not improve the classification accuracy. In addition, we were able to get reasonably good results using the 30 Hz flickering frequency comparing with 15 Hz flickering frequency that will give us the ability of having a shorter training and decision making time.