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