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  4. Deep Learning for Whole-Brain Cognitive Decoding
 
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2022
Conference Paper
Title

Deep Learning for Whole-Brain Cognitive Decoding

Abstract
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.
Author(s)
Müller, Klaus-Robert
Thomas, Armin W.
Samek, Wojciech  
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
Mainwork
10th International Winter Conference on Brain-Computer Interface, BCI 2022  
Conference
International Winter Conference on Brain-Computer Interface 2022  
DOI
10.1109/BCI53720.2022.9735047
Language
English
Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut HHI  
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