Sparse Group Representation Model for Motor Imagery EEG Classification

A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust classifier training. The calibration burden during data acquisition phase will most probably cause a...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2019-03, Vol.23 (2), p.631-641
Hauptverfasser:	Jiao, Yong, Zhang, Yu, Chen, Xun, Yin, Erwei, Jin, Jing, Wang, Xingyu, Cichocki, Andrzej
Format:	Artikel
Sprache:	eng
Schlagworte:	Brain Brain modeling Brain-computer interface (BCI) Brain-Computer Interfaces Calibration Classification common spatial pattern (CSP) Computer applications Data acquisition Data processing Datasets Dictionaries EEG electroencephalogram (EEG) Electroencephalography Electroencephalography - classification Electroencephalography - methods Feature extraction Human-computer interface Humans Image classification Imagination - physiology Implants Informatics Machine Learning Matrix methods Mental task performance Model accuracy motor imagery (MI) Motors Representations Signal Processing, Computer-Assisted sparse group representation model (SGRM) Training
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