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This chapter will propose a new paradigm for single-trial-electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) with motor imagery (MI) [1] tasks. Among such BCIs, the sensorimotor rhythm (SMR)-based ones, when using common spatial patterns (CSPs), require features over broad frequency bands, such as mu, beta and gamma rhythms [2]. Therefore, very high-dimensional feature vectors and continuous-valued patterns necessary for spatio‐ temporally checking the features [3,4] could yield an enormous amount of data and much computational time [5]. So, various data reduction such as downsampling [6,7] and optimal EEG channel configuration [8,9,10] have been investigated for the BCIs.

Equivalent-Current-Dipole-Source-Localization-Based BCIs with Motor Imagery