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...

Celý popis

Podrobná bibliografie

Vydáno v:	Measurement : journal of the International Measurement Confederation Ročník 223; s. 113673
Hlavní autoři:	Liu, Xiaolin, Sun, Ying, Zheng, Dezhi, Na, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.12.2023
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!