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
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Zusammenfassung: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 impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.
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Reviewed by: Chuandong Li, Southwest University, China
Edited by: Mei Liu, Multi-scale Medical Robotics Center Limited, China
Tingwen Huang, Shenzhen University, China
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2025.1557287