The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation
This addresses the challenge of individual variation and non-stationarity in EEG signals for visual BMI applications, offering incremental improvements in biomarker reliability.
The paper tackled the problem of unreliable EEG biomarkers in visual brain-machine interfaces by proposing the VEP Booster, a closed-loop AI system that refines visual stimuli based on real-time EEG feedback, resulting in an average 76.5% increase in SSVEP response across five subjects.
Effective visual brain-machine interfaces (BMI) is based on reliable and stable EEG biomarkers. However, traditional adaptive filter-based approaches may suffer from individual variations in EEG signals, while deep neural network-based approaches may be hindered by the non-stationarity of EEG signals caused by biomarker attenuation and background oscillations. To address these challenges, we propose the Visual Evoked Potential Booster (VEP Booster), a novel closed-loop AI framework that generates reliable and stable EEG biomarkers under visual stimulation protocols. Our system leverages an image generator to refine stimulus images based on real-time feedback from human EEG signals, generating visual stimuli tailored to the preferences of primary visual cortex (V1) neurons and enabling effective targeting of neurons most responsive to stimuli. We validated our approach by implementing a system and employing steady-state visual evoked potential (SSVEP) visual protocols in five human subjects. Our results show significant enhancements in the reliability and utility of EEG biomarkers for all individuals, with the largest improvement in SSVEP response being 105%, the smallest being 28%, and the average increase being 76.5%. These promising results have implications for both clinical and technological applications