Considerate motion imagination classification method using deep learning

In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distri...

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Veröffentlicht in:	PloS one 2022-10, Vol.17 (10), p.e0276526
Hauptverfasser:	Yan, Zhaokun, Yang, Xiangquan, Jin, Yu
Format:	Artikel
Sprache:	eng
Schlagworte:	Ablation Algorithms Analysis Biology and Life Sciences Brain Brain-Computer Interfaces Classification Computational linguistics Computer and Information Sciences Computer applications Correlation Deep Learning EEG Electrodes Electroencephalography Electroencephalography - methods Emotion recognition Engineering and Technology Euclidean geometry Euclidean space Feature extraction Graph representations Graphical representations Human-computer interface Imagination Imagination (Philosophy) Implants Interfaces Language processing Machine learning Medicine and Health Sciences Mental task performance Methods Natural language interfaces Neural networks Rehabilitation Research and Analysis Methods Signal classification Social Sciences User interface Wavelet transforms
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