提出BITE模型提升脑电分类鲁棒性，跨范式通用性强。
Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification
- 双向时频金字塔结构融合多尺度特征，增强神经模式识别
- 在4个不同范式上达到当前最优性能，跨被试泛化能力突出
- 适合需高鲁棒性的脑机接口应用，尤其适用于运动想象与稳态视觉诱发电位任务
现有脑电识别模型因数据集特异性与个体差异导致跨范式泛化能力差。为此，我们提出BITE（双向时频金字塔网络），一种端到端统一架构，具备强多流协同、金字塔时频注意力（PTFA）和双向自适应卷积。该框架独特融合：1）对齐的时频流保持与STFT的时间同步，实现双向建模；2）基于PTFA的多尺度特征增强，凸显关键神经模式；3）可学习融合的BiTCN捕捉前后向神经动态。BITE在四个差异显著范式（BCICIV-2A/2B、HGD、SD-SSVEP）上表现优异，兼具被试内准确率与跨被试泛化能力。作为统一架构，其同时实现运动想象与稳态视觉诱发电位任务的优异性能与极低计算开销。研究验证了范式对齐的谱时序处理对可靠脑机系统至关重要。代码开源：https://github.com/cindy-hong/BiteEEG。
原文摘要 · Abstract (English)
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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