Fuzzy ATRMAP classification of mental tasks using segmented and overlapped EEG signals

Visual inspection of EEG signals in their unprocessed form is still the predominant way of discriminating EEG patterns in the medical community and requires highly trained medical professionals. To overcome this problem, automatic EEG analysis using Fourier transform methods are popular since most EEG signals consist of the spectral power in the range of δ, θ, α and β, i.e. from 0 to 30 Hz. However, this method suffers from high noise sensitivity and is not suitable for short and variable length signal segments. We analyse EEG signals with time series analysis using autoregression techniques. We classify these extracted features for different mental tasks using a fuzzy ARTMAP classifier. We study the effects of different EEG segment or window lengths and different overlapping lengths on the overall performance of the classifier. Our results show that the segment length affects the performance and that overlapping the segments improves the performance greatly