SAT-Net: SincNet-Based Attentive Temporal Convolutional Network for Motor Imagery Classification

Brain-computer interfaces (BCIs) are a promising method for users to interact with machines using brain signals, primarily electroencephalography (EEG). Motor imagery (MI)-BCI, which decodes EEG signals induced by the user's imagination of moving body parts, has gained great attention due to its applicability in various fields such as robotics and rehabilitation. MI - EEG signals exhibit class-discriminative patterns such as event-related de/synchronization across spectral-spatio-temporal (SST) domains. Numerous studies adopted deep learning framework, especially convolutional neural network (CNN), for learning SST feature representations automatically in a data-driven manner. In particular, most of the CNN-based methods adopted temporal convolution to learn the spectral information, as it can act as a band-pass filter. In this paper, we propose SAT-Net, a SincNet-based attentive temporal convolutional network for motor imagery classification. The proposed method utilizes Sinc convolution from SincNet for explicit extraction of spectral information from the input EEG signals with high interpretability. Moreover, we adopt an attentive temporal convolutional network to effectively learn SST feature representations while making full use of temporal information. We evaluate our proposed SAT-Net on the public BCI Competition IV-2a dataset, comparing it not only to conventional CNN-based approaches but also to the state-of-the-art method. The experimental results, supported by statistical analysis, demonstrate that our approach outperforms the competing methods.