SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification
This addresses the problem of inter-subject variability in EEG signals for neurorehabilitation and assistive technologies, representing an incremental improvement over prior transformer-based approaches.
The paper tackles the challenge of robust cross-subject classification in EEG-based motor imagery for brain-computer interfaces by proposing the SSTAF Transformer, which achieves accuracies of 76.83% and 68.30% on two datasets, outperforming existing methods.
Brain-computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by enabling communication between the brain and external devices. However, the non-stationary nature of EEG signals and significant inter-subject variability cause substantial challenges for developing robust cross-subject classification models. This paper introduces a novel Spatial-Spectral-Temporal Attention Fusion (SSTAF) Transformer specifically designed for upper-limb motor imagery classification. Our architecture consists of a spectral transformer and a spatial transformer, followed by a transformer block and a classifier network. Each module is integrated with attention mechanisms that dynamically attend to the most discriminative patterns across multiple domains, such as spectral frequencies, spatial electrode locations, and temporal dynamics. The short-time Fourier transform is incorporated to extract features in the time-frequency domain to make it easier for the model to obtain a better feature distinction. We evaluated our SSTAF Transformer model on two publicly available datasets, the EEGMMIDB dataset, and BCI Competition IV-2a. SSTAF Transformer achieves an accuracy of 76.83% and 68.30% in the data sets, respectively, outperforms traditional CNN-based architectures and a few existing transformer-based approaches.