Comparison of Models for EEG-based Cross-Subject Movement Prediction (Student Conference Proceedings, 2025)
Brain-computer interfaces for robotic-assisted rehabilitation based on EEG recordings can play an important role in the rehabilitation of stroke patients. To effectively classify movement intentions from EEG signals, machine learning models are needed that can work with noisy and heterogeneous data. This work investigates four neural networks, namely the MLP, TKAN, EEGNet, and SincEEGNet, in their ability to predict movement intentions in intra- and cross-subject classification. Additionally, the amount of calibration data for adapting the pre-trained models of the cross-subject task to data of the target subject was explored. The model performance for intra-subject classification is highest with the EEGNet, however, SincEEGNet performs best in cross-subject classification. Calibrating the pre-trained models results in a performance gain with the overall highest accuracy of 88.1 % with SincEEGNet. Those results motivate to apply such models for EEG classification across subjects to reduce calibration times.
Keywords: neural networks; EEG data; amount of calibration data