A Lightweight Network with Domain Adaptation for Motor Imagery Recognition DOI Creative Commons
Xinmin Ding, Zenghui Zhang, Kun Wang

et al.

Entropy, Journal Year: 2024, Volume and Issue: 27(1), P. 14 - 14

Published: Dec. 27, 2024

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

Language: Английский

Fusion analysis of EEG-fNIRS multimodal brain signals: a multitask classification algorithm incorporating spatial-temporal convolution and dual attention mechanisms DOI
Xingbin Shi, Haiyan Wang, Baojiang Li

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2025, Volume and Issue: 74, P. 1 - 12

Published: Jan. 1, 2025

Language: Английский

Citations

0
Perception of Native vs. Non-Native Language and Non-Speech Sounds in One-Week-Old Neonates: An fNIRS Study DOI Creative Commons
Yuanye Ma, Jianming Zhang, Ruochen Dang

et al.

Brain Research Bulletin, Journal Year: 2025, Volume and Issue: 226, P. 111370 - 111370

Published: May 6, 2025

Language: Английский

Citations

0
A Lightweight Network with Domain Adaptation for Motor Imagery Recognition DOI Creative Commons
Xinmin Ding, Zenghui Zhang, Kun Wang

et al.

Entropy, Journal Year: 2024, Volume and Issue: 27(1), P. 14 - 14

Published: Dec. 27, 2024

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

Language: Английский

Citations

0