Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification

• URL: http://arxiv.org/abs/2510.10004v1
• Date: Sat, 11 Oct 2025 04:14:48 GMT
• Title: Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification
• Authors: Jiahui Hong, Siqing Li, Muqing Jian, Luming Yang,
• Abstract summary: BITE (Bidirectional Time-Freq Pyramid Network) is an end-to-end unified architecture featuring robust multistream synergy, pyramid time-frequency attention (PTFA), and bidirectional adaptive convolutions.<n>As a unified architecture, it combines robust performance across both MI and SSVEP tasks with exceptional computational efficiency.<n>Our work validates that paradigm-aligned spectral-temporal processing is essential for reliable BCI systems.
• Score: 2.512406961007489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing EEG recognition models suffer from poor cross-paradigm generalization due to dataset-specific constraints and individual variability. To overcome these limitations, we propose BITE (Bidirectional Time-Freq Pyramid Network), an end-to-end unified architecture featuring robust multistream synergy, pyramid time-frequency attention (PTFA), and bidirectional adaptive convolutions. The framework uniquely integrates: 1) Aligned time-frequency streams maintaining temporal synchronization with STFT for bidirectional modeling, 2) PTFA-based multi-scale feature enhancement amplifying critical neural patterns, 3) BiTCN with learnable fusion capturing forward/backward neural dynamics. Demonstrating enhanced robustness, BITE achieves state-of-the-art performance across four divergent paradigms (BCICIV-2A/2B, HGD, SD-SSVEP), excelling in both within-subject accuracy and cross-subject generalization. As a unified architecture, it combines robust performance across both MI and SSVEP tasks with exceptional computational efficiency. Our work validates that paradigm-aligned spectral-temporal processing is essential for reliable BCI systems. Just as its name suggests, BITE "takes a bite out of EEG." The source code is available at https://github.com/cindy-hong/BiteEEG.

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