Hilbert–Huang Transformation-based subject-specific time–frequency-space pattern optimization for motor imagery electroencephalogram classification

The advancement of brain–computer interfaces (BCIs) has narrowed the gap between humans and computers, allowing intentional interaction by monitoring and translating brain signals in real time. Among BCI approaches, motor imagery electroencephalogram (MI-EEG) systems are popular due to their non-inv...

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Bibliographic Details

Published in:	Measurement : journal of the International Measurement Confederation Vol. 223; p. 113673
Main Authors:	Liu, Xiaolin, Sun, Ying, Zheng, Dezhi, Na, Rui
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2023
Subjects:	Brain computer interface Electroencephalogram Hilbert–Huang transformation Motor imagery classification Time–frequency-space analysis Hilbert–Huang transformation Brain computer interface Time–frequency-space analysis Electroencephalogram Motor imagery classification
ISSN:	0263-2241
Online Access:	Get full text
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