A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

Journal: IEEE transactions on bio-medical engineering

Published Date: May 19, 2022

Abstract

OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional near-infrared spectroscopy (fNIRS) require novel approaches to decoding.

Authors

Ciaran Cooney

Intelligent Systems Research Centre, Ulster University, Londonderry BT48 7JL, UK.
Raffaella Folli

Institute for Research in Social Sciences, Ulster University, Jordanstown BT37 0QB, UK.
Damien Coyle

Intelligent Systems Research Centre, Ulster University, Londonderry BT48 7JL, UK.

Keywords

Brain-Computer Interfaces Deep Learning Electroencephalography Humans Neural Networks, Computer Speech

External Resources

View on PubMed Access via DOI PubMed (34874850)

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

Abstract

Authors

Keywords

External Resources

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