Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

Antonio Maria Chiarelli; Pierpaolo Croce; Arcangelo Merla; Filippo Zappasodi

doi:10.1088/1741-2552/aaaf82

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Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

Antonio Maria Chiarelli Pierpaolo Croce Arcangelo Merla Filippo Zappasodi

Year: 2018 Journal: Journal of Neural Engineering Vol: 15 (3)Pages: 036028-036028 Publisher: IOP Publishing

DOI: 10.1088/1741-2552/aaaf82

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Abstract

BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Keywords:

Brain–computer interface Motor imagery Electroencephalography Computer science Functional near-infrared spectroscopy Artificial intelligence Neuroimaging Classifier (UML) Modality (human–computer interaction) Deep learning Pattern recognition (psychology) Brain activity and meditation Interface (matter) Speech recognition Cognition Psychology Neuroscience Prefrontal cortex

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Topics

EEG and Brain-Computer Interfaces

Life Sciences → Neuroscience → Cognitive Neuroscience

Neuroscience and Neural Engineering

Life Sciences → Neuroscience → Cellular and Molecular Neuroscience

Optical Imaging and Spectroscopy Techniques

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

Abstract

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Citation History

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