Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

The time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is a...

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Veröffentlicht in:	Frontiers in neuroscience Jg. 17; S. 1292724
Hauptverfasser:	Zhang, Shaorong, Wang, Qihui, Zhang, Benxin, Liang, Zhen, Zhang, Li, Li, Linling, Huang, Gan, Zhang, Zhiguo, Feng, Bao, Yu, Tianyou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Lausanne Frontiers Research Foundation 03.11.2023 Frontiers Media S.A
Schlagworte:	Classification Datasets Deep learning EEG EEG decoding Electroencephalography Feature selection Frequency dependence high-dimensional small-sample Mental task performance motor imagery Motor task performance nonconvex regularization Regularization methods Signal processing Sparsity
ISSN:	1662-453X, 1662-4548, 1662-453X
Online-Zugang:	Volltext
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