RESUMO
OBJECTIVE: Motor Imagery (MI) represents one major paradigm of Brain-Computer Interfaces (BCIs) in which users rely on their Electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology. APPROACH: This study focuses on enhancing cross-subject MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications. MAIN RESULTS: To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in cross-subject accuracy outperforming state-of-the-art methods. SIGNIFICANCE: This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.
