Separation of Stimulus-Specific Patterns in Electroencephalography Data Using Quasi-Supervised Learning

Abstract

In this study separation of the electroencephalography data recorded under different visual stimuli is investigated using the quasi-supervised learning algorithm. The quasi-supervised learning algorithm estimates the posterior probabilities associated with the different stimuli, thus identifying the EEG data samples that are exclusively specific to their respective stimuli directly and automatically from the data. The data used in this study contains 32 channels EEG recording under six different visual stimuli in random successive order. In our study, we have first constructed EEG profiles to represent instantaneous brain activity from the EEG data by various combinations of independent component analysis and the wavelet transform following data preprocessing. Then, we have applied the binary and M-ary quasi-supervised learning to identify condition-specific EEG profiles in different comparison scenarios. The results reveal that the quasi-supervised learning algorithm is successful in capturing the distinction between the samples. In addition, feature extraction using independent component analysis increased the performance of the quasi-supervised learning and the wavelet decomposition revealed the different frequency bands of the features, making more explicit the separation of the samples. The best results we obtained by combining the wavelet decomposition and the independent component analysis before the quasisupervised learning algorithm.