  Research about brain activities involving spoken word production is
considerably underdeveloped because of the undiscovered characteristics of
speech artifacts, which contaminate electroencephalogram (EEG) signals and
prevent the inspection of the underlying cognitive processes. To fuel further
EEG research with speech production, a method using three-mode tensor
decomposition (time x space x frequency) is proposed to perform speech artifact
removal. Tensor decomposition enables simultaneous inspection of multiple
modes, which suits the multi-way nature of EEG data. In a picture-naming task,
we collected raw data with speech artifacts by placing two electrodes near the
mouth to record lip EMG. Based on our evaluation, which calculated the
correlation values between grand-averaged speech artifacts and the lip EMG,
tensor decomposition outperformed the former methods that were based on
independent component analysis (ICA) and blind source separation (BSS), both in
detecting speech artifact (0.985) and producing clean data (0.101). Our
proposed method correctly preserved the components unrelated to speech, which
was validated by computing the correlation value between the grand-averaged raw
data without EOG and cleaned data before the speech onset (0.92-0.94).
