EEGBiFormer：混合 CNN-Transformer 实时解码 16 通道 EEG 注意力状态
EEGBiFormer: Real-Time EEG Attention Classification via Hybrid CNN-Transformer with EEG-Informed Auditory Protocol Validation
有人把 EEG 硬件、注意力分类模型和音频干预整套跑通了，实时延迟不到 100 毫秒，37 人实验里注意力分数提升 20%，做脑机接口的可以看看。
arXiv 论文 EEGBiFormer 用 CNN-Transformer 混合架构对 16 通道 EEG 信号做专注/非专注的二分类，窗口级验证准确率 91.3%，F1 达 94.5%，17 个独立会话的录音级准确率为 88.2%。系统端到端延迟低于 100 ms，可支持实时响应。基于模型对听觉刺激的专注置信度排序生成的音频协议，在 37 人被试内实验中使综合注意力得分比静音对照提升 20%，效应量为中到大。
EEGBiFormer: Real-Time EEG Attention Classification via Hybrid CNN-Transformer with EEG-Informed Auditory Protocol Validation
Sustained attention is critical for academic performance, yet existing auditory support tools are static and not grounded in real-time neural evidence. This paper presents EEGBiFormer, a hybrid CNN-Transformer model for realtime binary classification of attention states, focus versus nonfocus, from 16-channel EEG signals. A custom analog front-end acquisition board was designed and built to collect a task-based EEG dataset. Before model training, spectral and connectivity analysis, using Power Spectral Density and Phase Locking Value, confirmed distinct neural signatures between attention states, providing physiological grounding for the classification approach. EEGBiFormer achieved 91.3 percent window-level validation accuracy with an F1 score of 94.5 percent, and 88.2 percent recording-level accuracy across 17 independent sessions. End-to-end system latency was measured below 100 ms, supporting real-time responsiveness. The trained model was further used to objectively rank auditory stimuli by focus-state confidence, yielding EEG-informed audio protocols. In a paired within-subject behavioral study with 37 participants, these protocols produced a statistically significant 20 percent improvement in composite attention scores over silent controls, with a medium-to-large effect size. This work demonstrates a complete pipeline from custom EEG hardware through neural signal analysis, deep learning classification, and evidence-based auditory protocol design.