Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

• Published in: Bioengineering (Basel) Vol. 11; no. 4; p. 347
• Main Authors: Zhang, Baiwen, Xu, Meng, Zhang, Yueqi, Ye, Sicheng, Chen, Yuanfang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.04.2024
• Subjects: Accuracy; Algorithms; Analysis; Attention-ProNet; Brain; Calibration; Classification; Computer applications; Data augmentation; Datasets; EEG; Electroencephalography; Event-related potentials; Feature extraction; Human-computer interface; Human-machine systems; hybrid attention mechanism; Implants; Learning; Machine learning; Methods; Neural networks; Paradigms; prototype networks; Prototypes; Rapid serial visual presentation; rapid serial visual presentation (RSVP); Target recognition; zero-calibration (ZC); China; Attention-ProNet; hybrid attention mechanism; prototype networks; zero-calibration (ZC); rapid serial visual presentation (RSVP)
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11040347

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.