Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach

Jul 19, 2024Journal of neural engineering

Improving motor imagination brain-computer interfaces using advanced neural network and data generation methods without needing calibration

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Abstract

Using modified deep learning techniques and synthetic data, the proposed method enhances cross-subject accuracy in motor imagery EEG classification.

Motor imagery brain-computer interfaces typically require individual subject data for effective training.
The proposed method utilizes EEG spectrum images and employs Wasserstein Generative Adversarial Networks for data augmentation.
Synthetic images generated by the WGAN expand the training dataset without requiring calibration data from the target subject.
Evaluation on established BCI competition datasets shows improved cross-subject accuracy compared to existing methods.
This approach may facilitate the development of calibration-free brain-computer interface systems.

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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.This study focuses on enhancing cross-subject (CS) 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.To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, 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 CS accuracy outperforming state-of-the-art methods.This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications. Objective. Approach. Main results. Significance.

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Toward calibration-free motor imagery brain–computer interfaces: a VGG-based convolutional neural network and WGAN approach

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