An anti-interference EEG-EOG hybrid detection approach for motor image identification and eye track recognition

In this paper, an anti-interference EEG-EOG hybrid detection approach is adopted for motor image identification and eye track recognition. We present a wave trap filter and a band pass filter to suppress power frequency interference and white noise. Meanwhile, the filter can still separate EEG and EOG signal from hybrid signal. Then, the features of EEG/EOG signal are extracted by the wavelet transform algorithm, and classified by the linear discriminant analysis (LDA) algorithm. The effectiveness of the signal process method described in this paper can be clearly observed through both of simulations and experiments, and the accuracy rate of pattern recognition is demonstrated in the end of the paper.