Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression

Objective. Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to deco...

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Veröffentlicht in:	Journal of neural engineering 2020-08, Vol.17 (4), p.46029-046029
Hauptverfasser:	Chu, Yaqi, Zhao, Xingang, Zou, Yijun, Xu, Weiliang, Song, Guoli, Han, Jianda, Zhao, Yiwen
Format:	Artikel
Sprache:	eng
Schlagworte:	brain-machine interface motor imagery EEG partial least squares regression Riemannian geometry features same upper limb
Online-Zugang:	Volltext
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