A novel explainable machine learning approach for EEG-based brain-computer interface systems

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close hand’s motion preparation. To this end, cortical EEG source signals in the motor cortex (evaluated in the 1-s window preceding movement onset) are extracted by solving inverse problem through beamforming. EEG so...

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Veröffentlicht in:	Neural computing & applications Jg. 34; H. 14; S. 11347 - 11360
Hauptverfasser:	Ieracitano, Cosimo, Mammone, Nadia, Hussain, Amir, Morabito, Francesco Carlo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2022 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Artificial neural networks Beamforming Brain research Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Electroencephalography Hands Human-computer interface Image Processing and Computer Vision Inverse problems Investigations Machine learning Neural networks Occlusion Probability and Statistics in Computer Science S.I. : Healthcare Analytics Sensitivity analysis Deep learning Beamforming Brain–computer interface Explainable machine learning
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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