MI-LTN: A Neurosymbolic Framework for Enhanced EEG Feature Extraction and Model Interpretability in MI-BCI

Brain-Computer Interface (BCI) is a cutting-edge technology that facilitates human-computer interaction. Motor Imagery Electroencephalogram (MI-EEG) decoding technology has emerged as a significant direction in BCI research. Despite the remarkable advancements in deep learning for EEG signal decoding in recent years, two major challenges persist: the comprehensive representation and extraction of features, and the lack of interpretability. To address these issues, we propose a novel neurosymbolic framework termed MI-LTN (Motor Imagery Logic Tensor Network), incorporate logical constraints into the training model using the Logic Tensor Network (LTN) and employ Shapley values to evaluate and adjust the importance of channels. Our experimental results show that MI-LTN achieves classification accuracies of 86.00% and 88.84% on the BCI IV 2a and BCI IV 2b datasets, respectively. These results demonstrate the great potential of LTN in MI-EEG decoding.