EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory

EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory

To address the limitations of traditional unimodal brain-computer interface BCI) technologies based on electroencephalography (EEG) such as low spatial resolution and high susceptibility to noise an increasing number of neuroscience-driven studies have begun to focus on BCI systems that fuse EEG sig...

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Bibliographic Details
Main Authors: Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Neuroscience Informatics
Subjects:
Brain-computer interface
Neuroscience
Near-infrared spectroscopy
Gated recurrent unit
Dempster-Shafer theory
Online Access:http://www.sciencedirect.com/science/article/pii/S2772528625000299
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