EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code freely accessible at https://github.com/AbbasSalami/EEG-ITNet.

Automated summary

In recent years, neural networks and deep architectures have gained attention in brain-computer interfaces for EEG signal analysis. End-to-end models are favored over traditional approaches, as they eliminate the need for prior information and feature extraction. However, the lack of interpretability and the sensitivity of deep neural networks pose challenges for their adoption in the neuroscience community. This study proposes a new deep learning architecture called EEG-ITNet, which extracts rich information from multi-channel EEG signals with less complexity than existing models. Evaluation results show improved classification accuracy compared to competitors, and the validity of network interpretation is supported from a neuroscientific perspective.