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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| Vydáno v: | Frontiers in neuroscience Ročník 19; s. 1557287 |
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06.03.2025
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Guan, Zhi-Hong Li, Junyan Hu, Bin |
| AuthorAffiliation | 3 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology , Wuhan , China 1 School of Future Technology, South China University of Technology , Guangzhou , China 2 Guangdong Artificial Intelligence and Digital Economy Laboratory , Guangzhou , China |
| AuthorAffiliation_xml | – name: 1 School of Future Technology, South China University of Technology , Guangzhou , China – name: 2 Guangdong Artificial Intelligence and Digital Economy Laboratory , Guangzhou , China – name: 3 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology , Wuhan , China |
| Author_xml | – sequence: 1 givenname: Junyan surname: Li fullname: Li, Junyan – sequence: 2 givenname: Bin surname: Hu fullname: Hu, Bin – sequence: 3 givenname: Zhi-Hong surname: Guan fullname: Guan, Zhi-Hong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40115889$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1088/1741-2552/aab2f2 10.1016/j.cub.2010.06.017 10.3389/fnins.2016.00430 10.1088/1741-2552/aace8c 10.3389/fncom.2015.00099 10.1038/s41467-021-27653-2 10.3389/fnins.2022.881598 10.1109/TBME.2024.3474049 10.1109/TBME.2004.827088 10.1109/MSP.2019.2931595 10.1016/j.neuroimage.2023.119893 10.1109/TNNLS.2018.2870553 10.1109/TBME.2022.3193277 10.1109/MM.2018.112130359 10.1002/047134608X.W8278 10.3389/fnins.2023.1122661 10.1073/pnas.83.14.5326 10.1109/TBME.2023.3258606 10.1109/ACCESS.2018.2870052 10.1016/j.celrep.2023.113142 10.1109/TBME.2024.3432934 10.1109/TII.2022.3197419 10.1109/TAFFC.2022.3164516 10.1109/TIM.2023.3300471 10.48550/arXiv.2407.03177 10.1177/2096595819896200 10.1109/TKDE.2021.3070203 10.1038/s41586-019-1424-8 10.1152/physrev.00027.2016 10.1109/TBME.2022.3182588 10.1109/TCYB.2019.2946914 10.1038/s41551-020-0542-9 10.1109/21.87054 10.1109/MSP.2008.4408441 10.1109/JETCAS.2020.3037951 |
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| Keywords | multi-task learning impulsive neural network brain-computer interface electroencephalogram (EEG) bidirectional associative memory |
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| Title | AM-MTEEG: multi-task EEG classification based on impulsive associative memory |
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