دورية أكاديمية
Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface
| العنوان: | Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface |
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| المؤلفون: | Baiwen Zhang, Meng Xu, Yueqi Zhang, Sicheng Ye, Yuanfang Chen |
| المصدر: | Bioengineering, Vol 11, Iss 4, p 347 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Technology LCC:Biology (General) |
| مصطلحات موضوعية: | Attention-ProNet, hybrid attention mechanism, rapid serial visual presentation (RSVP), zero-calibration (ZC), prototype networks, Technology, Biology (General), QH301-705.5 |
| الوصف: | The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2306-5354 |
| Relation: | https://www.mdpi.com/2306-5354/11/4/347; https://doaj.org/toc/2306-5354 |
| DOI: | 10.3390/bioengineering11040347 |
| URL الوصول: | https://doaj.org/article/7ec02ee216fb424286dd88755447d0a7 |
| رقم الأكسشن: | edsdoj.7ec02ee216fb424286dd88755447d0a7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23065354 |
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| DOI: | 10.3390/bioengineering11040347 |