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The paper applies a hybrid CNN-Transformer to classify focus versus nonfocus states from 16-channel EEG, with a sub-100 ms reported latency. For ML readers, the useful details are the evaluation choices: window-level validation accuracy (91.3%) is higher than recording-level accuracy (88.2%) across 17 sessions, and it is unclear whether results hold across unseen subjects. The 37-participant behavioral result (a 20% gain over silent controls) is a separate, smaller-scale claim.
EEGBiFormer: Real-Time EEG Attention Classification via Hybrid CNN-Transformer with EEG-Informed Auditory Protocol Validation
arXiv:2610.33832v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2610.33832 [cs.LG]
(or arXiv:2610.33832v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33832 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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