Fuzzy Feature Extraction for Multichannel EEG Classification

Abstract

EEG signals (EEGs) are usually collected by placing multiple electrodes at various positions along the scalp as multichannel data. Given that many channels are collected for each single-trial, the multichannel EEG classification problem can be treated as multivariate time series classification problem. For multichannel EEG data to be more accurately classified, we propose an algorithm, called the fuzzy multichannel EEG classifier (FMCEC). This algorithm can take into consideration the interaction among different signals collected at different time instants and locations on the skull when constructing a classifier. The FMCEC first preprocess raw EEG data by eliminating noise by discretization of the data. It then performs fuzzification of the resulting discretized data to capture imprecision and vagueness in the data. Given the fuzzified data, FMCEC then discovers intrachannel patterns within each channel and then interchannel patterns between different channels of EEGs. The discovered patterns, which are represented as fuzzy temporal patterns, are then used to characterize and differentiate between different classes of multichannel EEG data. To evaluate the effectiveness of FMCEC, we tested it with several sets of real EEG datasets. The results show that the algorithm can be a promising tool for the classification of multichannel EEG data.