Real-Time Feedback-Based Motor Imagery EEG Acquisition Method Using Brain Foundation Model

Motor imagery (MI) is widely used in brain-computer interfaces (BCIs), but conventional EEG acquisition often produces low-quality signals. Feedback methods improve EEG quality, but typically need initial session data to train classifiers. This increases complexity and delays feedback. We solve this using a brain foundation model (EEGPT), which leverages self-supervised pre-training on large-scale MI data to enable accurate classification with minimal new samples. Our method combines EEGPT's encoder (frozen) for feature extraction with an online updated Support Vector Machine (SVM) classifier. This provides real-time feedback during EEG acquisition. Our approach shows superior efficiency and accuracy under limited data versus models trained from scratch on BNCI2014 and ZHOU2016 datasets. In a 5 -class MI experiment with 7 participants, data acquired with our feedback method showed 4.1% higher average accuracy across multiple models. Our method processes EEG quickly (inference: 9.92 ± 0.51 ms per sample; SVM training: 0.276 ± 0.008 s), enabling early high-quality feedback with minimal calibration. This significantly improves BCI efficiency and practical deployment.