Multimodal fNIRS-EEG Classification Using Deep Learning Algorithms for Brain-Computer Interfaces Purposes

Marjan Saadati; Jill Nelson; Hasan Ayaz

doi:10.1007/978-3-030-20473-0_21

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Multimodal fNIRS-EEG Classification Using Deep Learning Algorithms for Brain-Computer Interfaces Purposes

Marjan Saadati Jill Nelson Hasan Ayaz

Year: 2019 Advances in intelligent systems and computing Pages: 209-220 Publisher: Springer Nature

DOI: 10.1007/978-3-030-20473-0_21

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Keywords:

Brain–computer interface Motor imagery Electroencephalography Computer science Feature selection Artificial intelligence Neuroimaging Preprocessor Brain activity and meditation Speech recognition Pattern recognition (psychology) Psychology Neuroscience

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EEG and Brain-Computer Interfaces

Life Sciences → Neuroscience → Cognitive Neuroscience

Neuroscience and Neural Engineering

Life Sciences → Neuroscience → Cellular and Molecular Neuroscience

Advanced Memory and Neural Computing

Physical Sciences → Engineering → Electrical and Electronic Engineering

Multimodal fNIRS-EEG Classification Using Deep Learning Algorithms for Brain-Computer Interfaces Purposes

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Citation History

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