Motor task-to-task transfer learning for motor imagery brain-computer interfaces

Gwon, Daeun; Ahn, Minkyu

doi:10.1016/j.neuroimage.2024.120906

NeuroImage

2024

DOI: 10.1016/j.neuroimage.2024.120906

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Motor task-to-task transfer learning for motor imagery brain-computer interfaces

Daeun Gwon,

Minkyu Ahn

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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition

Ding,

Zhang,

Wang

et al. 2024

Entropy

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Brain–computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model’s parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model’s cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.

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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition

Ding,

Zhang,

Wang

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

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Motor task-to-task transfer learning for motor imagery brain-computer interfaces

Cited by 1 publication

References 60 publications

A Lightweight Network with Domain Adaptation for Motor Imagery Recognition

A Lightweight Network with Domain Adaptation for Motor Imagery Recognition

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