Fig. 1

Brain-computer interface systems. Brain-computer interface systems use state-of-the-art machine learning methods to decode brain activity. A brain-computer interface system is realized using several components: (1) brain signal activity acquisition: electroencephalogram (EEG), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), etc.; (2) signal processing: band-pass filtering, outlier removal, artifact correction, normalization, etc.; (3) feature extraction: gain task-relevant information from acquired data; (4) classification/regression: decode the intended action of the subject by applying machine learning methods; (5) control commands to external devices: screen, wheelchair, exoskeleton, etc.; (6) feedback: the subject receives feedback about how well he/she performed in a certain training task