Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Michael Hersche, Tino Rellstab, Pasquale D. Schiavone, Lukas Cavigelli, Luca Benini & Abbas Rahimi
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain–computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and...
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