Deep Learning for Whole-Brain Cognitive Decoding
Klaus-Robert Müller; Armin W. Thomas; Wojciech Samek
Accurately decoding brain activities is both a challenge for machine learning and a potential vehicle for gaining insight into complex cognitive brain states and their dynamics. In this brief note, we will touch upon selected recent directions of our research where machine learning techniques help to analyze brain measurements from EEG, fNIRS and fMRI typically targeting BCI applications. We owe these steps that we are summarizing mainly to activities of members of the BBCI team and their collaborators. Clearly, unavoidably and intentionally this abstract will have a high overlap to prior own contributions as it reports about and discusses these novel ideas and directions.
