Multiway analysis of EEG artifacts based on Block Term Decomposition

Neural information recorded from electroencephalogram (EEG) provides new possibilities for diagnosis of brain abnormalities, cognitive monitoring, etc. However, many artifacts, such as eye blink and muscle movements, impact and contaminate EEG data. While traditional techniques proposed for artifact removal identified artifact on two-way data, (spatial x temporal), multidimensional nature of EEG data (spatial x temporal x spectral x condition x trial) is overlooked. In this work, we investigate the use of multiway analysis/tensor factorization on the extended EEG tensor (spatial x temporal x spectral), which is constructed from continuous wavelet transform, using Block Term Decomposition (BTD) of rank-(L_r, L_r, 1) for artifact removal. Eight different carefully designed experiments to study artifact typically produced by voluntarily, and sometimes involuntarily, behaviors using a subject were performed and analyzed. After the BTD decomposition, artifacted components are automatically identified removed using spatial and temporal features. The reconstructed signal from proposed method suppresses artifact while retains the signal texture of eight types of artifact investigated.