Neural network classification of late gamma band electroencephalogram features

This paper investigates the feasibility of using neural network (NN) and late gamma band (LGB) electroencephalogram (EEG) features extracted from the brain to identify the individuality of subjects. The EEG signals were recorded using 61 active electrodes located on the scalp while the subjects perceived a single picture. LGB EEG signals occur with jittering latency of above 280 ms and are not time-locked to the triggering stimuli. Therefore, LGB EEG could only be computed from single trials of EEG signals and the common method of averaging across trials to remove undesired background EEG (i.e. noise) is not possible. Here, principal component analysis has been used to extract single trials of EEG signals. Zero phase Butterworth filter and Parseval's time-frequency equivalence theorem were used to compute the LGB EEG features. These features were then classified by backpropagation and simplified fuzzy ARTMAP NNs into different categories that represent the individuality of the subjects. The results using a tenfold cross validation scheme gave a maximum classification of 97.33% when tested on 800 unseen LGB EEG features from 40 subjects. This pilot investigation showed that the method of identifying the individuality of subjects using NN classification of LGB EEG features is worth further study.