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 |
---|---|
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 |
تدمد: | 23065354 |
---|---|
DOI: | 10.3390/bioengineering11040347 |