Tensor Decomposition for Imagined Speech Discrimination in EEG

Most of the researches in Electroencephalogram(EEG)-based Brain-Computer Interfaces (BCI) are focused on the use of motor imagery. As an attempt to improve the control of these interfaces, the use of language instead of movement has been recently explored, in the form of imagined speech. This work aims for the discrimination of imagined words in electroencephalogram signals. For this purpose, the analysis of multiple variables of the signal and their relation is considered by means of a multivariate data analysis, i.e., Parallel Factor Analysis (PARAFAC). In previous works, this method has demonstrated to be useful for EEG analysis. Nevertheless, to the best of our knowledge, this is the first attempt to analyze imagined speech signals using this approach. In addition, a novel use of the extracted PARAFAC components is proposed in order to improve the discrimination of the imagined words. The obtained results, besides of higher accuracy rates in comparison with related works, showed lower standard deviation among subjects suggesting the effectiveness and robustness of the proposed method. These results encourage the use of multivariate analysis for BCI applications in combination with imagined speech signals.