TF-HiTNet: A Temporal-Frequency Hierarchical Transformer Network for EEG Motor Imagery Classification

Electroencephalogram (EEG) motor imagery decoding, serving as the primary non-invasive modality for exploring brain-computer interfaces, has gained increasing attention. Previous research has achieved significant breakthroughs in the extraction and classification of features related to motor imagery. However, effectively integrating temporal-frequency patterns and capturing long-term dependencies across the entire sequence remain open challenges. To address these issues, we propose a novel Temporal-Frequency Hierarchical Transformer Network (TF-HiTNet) for EEG motor imagery classification. TF-HiTNet leverages a hierarchical transformer architecture and a feature fusion module to effectively extract and integrate temporal and frequency features from EEG signals. This approach captures both local features within EEG segments and global patterns across segments, while simultaneously considering information in both the time and frequency domains. Evaluation on the BCI4-2A and GigaDB datasets demonstrates the effectiveness of TF-HiTNet, achieving an average performance of 79.7% and 83.1%, respectively. Our experiments validate that the hierarchical transformer architecture can effectively learn the relationships between low-level and high-level features, while the time-frequency fusion module significantly improves the accuracy of motor imagery classify-cation.