Abstract
Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain–computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression. In this paper the L1-Penalized NPLS is proposed for sparse BCI system calibration, allowing uniting the projection technique with an effective selection of subset of features. The L1-Penalized NPLS was applied for the binary self-paced BCI system calibration, providing selection of electrodes subset. Our BCI system is designed for animal research, in particular for research in non-human primates.
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