A study on the application of contrastive learning in the brain-computer interface of motor imagery

Siyi Chen

doi:10.1117/12.3004989

16 August 2023 A study on the application of contrastive learning in the brain-computer interface of motor imagery

Siyi Chen

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871O (2023) https://doi.org/10.1117/12.3004989
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

In brain-computer interface (BCI) systems, acquiring EEG signals is relatively easy, but labeling EEG signals is challenging. Supervised learning is widely used in existing BCI research, but its generalization ability is poor due to the influence of labeled data, which is not conducive to the promotion and application of BCI. In the fields of computer vision and natural language processing, self-supervised learning is becoming a popular research direction that can use unlabeled data for learning. In this study, we propose a self-supervised contrastive learning method for identifying motor imagery states by performing different data transformation operations on EEG data to generate positive and negative sample pairs. This allows the pre-trained network to learn general feature representations from the signals, enabling it to converge faster and achieve better classification performance when performing downstream tasks. The network's performance is significantly related to the amount of unlabeled data used in the self-supervised learning process. Through the evaluation of the collected data, we achieved an accuracy of 82.56% in classifying motor imagery states with 40% amount of labeled data, demonstrating the practical value of our method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyi Chen "A study on the application of contrastive learning in the brain-computer interface of motor imagery", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871O (16 August 2023); https://doi.org/10.1117/12.3004989

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KEYWORDS

Data modeling

Education and training

Electroencephalography

Performance modeling

Brain-machine interfaces

Feature extraction

Image classification

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