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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition
Xinmin Ding1,2, Zenghui Zhang1, Kun Wang1,3
1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300384, China.
Entropy (Basel, Switzerland)
|January 24, 2025
Summary
This study introduces a novel brain-computer interface (BCI) method using a lightweight convolutional neural network (CNN) and domain adaptation to improve motor imagery recognition. The approach enhances cross-subject adaptability and real-time performance for practical assistive applications.
Area of Science:
- Neuroscience
- Computer Science
- Biomedical Engineering
Background:
- Brain-computer interfaces (BCI) are crucial for motor control and assistive technologies.
- Traditional BCI methods struggle with long training times and poor cross-subject adaptability.
- These limitations hinder the widespread practical application of BCI systems.
Purpose of the Study:
- To develop an innovative BCI method for motor imagery recognition.
- To address challenges of prolonged training and limited cross-subject adaptability in existing methods.
- To enhance real-time performance and computational efficiency for practical BCI applications.
Main Methods:
- A lightweight convolutional neural network (CNN) was combined with domain adaptation techniques.
- A feature extraction module was designed to efficiently process source and target domain data.
- Domain adversarial training was employed to learn domain-invariant features and align sample distributions.
Main Results:
- The proposed method achieved an average accuracy of 87.76% in a three-class motor imagery classification task on an fNIRS dataset.
- Lightweight experiments confirmed the method's potential for optimizing model structure and data feature selection.
- The approach demonstrated significant enhancement in cross-subject generalization ability.
Conclusions:
- The innovative BCI method effectively overcomes limitations of traditional approaches.
- The combination of lightweight CNN and domain adaptation offers improved real-time performance and cross-subject adaptability.
- This research shows significant potential for practical applications in motor imagery recognition systems.
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