Pattern classification of time-series EEG signals using neural networks

This paper proposes a pattern classification method of time-series EEG signals using neural networks. To achieve successful classification for non-stationary EEG signals, a new network structure that combines a probabilistic neural network and recurrent neural filters is used. This network is suitable for expressing statistical and time-varying characteristics of time-series EEG signals. In the experiments, two types of photic stimulation caused by eye opening/closing and by artificial light are used to measure the EEG data. It is shown that the proposed network can achieve high classification performance