Efficient implementation of RMVB for eyeblink artifacts removal of EEG via STF-TS modeling

In this paper, eyeblink artifacts of a multi-channel electroencephalogram (EEG) are extracted and removed by employing a spatial filter designed using the proposed efficient method via the robust minimum variance beamforming (RMVB). Unlike the conventional method where the spatial filter is designed using the correlation and prior knowledge of eyeblink from all channels of EEG, the spatial filter is obtained by simultaneously taking into consideration the local correlation and prior knowledge of each groups of EEG channels. The prior knowledge is obtained by taking into account the space-time-frequency information via the STF-TS model. Simulation results show that, by using less computational complexity, the proposed method can efficiently remove eyeblink artifacts from the multi-channel EEG as well as the conventional method.