AUTHOR=Li Junyan , Hu Bin , Guan Zhi-Hong 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1557287

DOI=10.3389/fnins.2025.1557287

ISSN=1662-453X

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.