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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Vydané v:	Measurement : journal of the International Measurement Confederation Ročník 223; s. 113673
Hlavní autori:	Liu, Xiaolin, Sun, Ying, Zheng, Dezhi, Na, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.12.2023
Predmet:	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
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