AM-MTEEG: multi-task EEG classification based on impulsive associative memory

Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsiv...

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Veröffentlicht in:	Frontiers in neuroscience Jg. 19; S. 1557287
Hauptverfasser:	Li, Junyan, Hu, Bin, Guan, Zhi-Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 06.03.2025 Frontiers Media S.A
Schlagworte:	Accuracy Artificial intelligence Associative learning bidirectional associative memory Brain research brain-computer interface Classification Datasets Deep learning EEG electroencephalogram (EEG) Electroencephalography Event-related potentials impulsive neural network Machine learning Memory Mental task performance multi-task learning Neural networks Neurons Neuroscience Neurosciences Guangzhou China China multi-task learning impulsive neural network brain-computer interface electroencephalogram (EEG) bidirectional associative memory
ISSN:	1662-453X, 1662-4548, 1662-453X
Online-Zugang:	Volltext
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