Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training
文献类型：期刊论文
| 作者 | Ni, Ziyi4,5; Xu, Jiaming4,5 ; Wu, Yuwei2,3; Li, Mengfan2,3; Xu, Guizhi2,3; Xu, Bo1,4,5
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
 |
| 出版日期 | 2022 |
| 卷号 | 30页码:369-379 |
| 关键词 | Brain modeling Electroencephalography Visualization Training Task analysis Feature extraction Adaptation models Brain-computer interface temporal modeling adversarial training cross-subject cross-state |
| ISSN号 | 1534-4320 |
| DOI | 10.1109/TNSRE.2022.3150007 |
| 通讯作者 | Xu, Bo(xubo@ia.ac.cn) |
| 英文摘要 | Brain-computer interface (BCI) is a useful device for people without relying on peripheral nerves and muscles. However, the performance of the event-related potential (ERP)-based BCI declines when applying it to real environments, especially in cross-state and cross-subject conditions. Here we employ temporal modeling and adversarial training to improve the visual ERP-based BCI under different mental workload states and to alleviate the problems above. The rationality of our method is that the ERP-based BCI is based on electroencephalography (EEG) signals recorded from the scalp's surface, continuously changing with time and somewhat stochastic. In this paper, we propose a hierarchical recurrent network to encode all ERP signals in each repetition at the same time and model them with a temporal manner to predict which visual event elicited an ERP. The hierarchical architecture is a simple yet effective method for organizing recurrent layers in a deep structure to model long sequence signals. Taking a cue from recent advances in adversarial training, we further applied dynamic adversarial perturbations to create adversarial examples to enhance the model performance. We conduct our experiments on one published visual ERP-based BCI task with 15 subjects and 3 different auditory workload states. The results indicate that our hierarchical method can effectively model the long sequence EEG raw data, outperform the baselines on most conditions, including cross-state and cross-subject conditions. Finally, we show how deep learning-based methods with limited EEG data can improve ERP-based BCI with adversarial training. Our code is available at https://github.com/aispeech-lab/VisBCI. |
| WOS关键词 | DISCRIMINANT-ANALYSIS ; COMPUTER ; P300 ; CLASSIFICATION ; INTERFACE |
| 资助项目 | National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[61806070] ; National Natural Science Foundation of China[51977060] ; Natural Science Foundation of Hebei Province[F2021202003] ; Technology Nova of Hebei University of Technology[JBKYXX2007] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070000] |
| WOS研究方向 | Engineering ; Rehabilitation |
| 语种 | 英语 |
| WOS记录号 | WOS:000757847700003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; Technology Nova of Hebei University of Technology ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47879]  |
| 专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| 通讯作者 | Xu, Bo |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 2.Hebei Univ Technol, Key Lab Electromagnet Field & Elect Apparat Relia, Tianjin 300130, Peoples R China 3.Hebei Univ Technol, State Key Lab Oratory Reliabil & Intelligence Ele, Tianjin Key Lab Bioelectromagnet Technol & Intell, Tianjin 300130, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ni, Ziyi,Xu, Jiaming,Wu, Yuwei,et al. Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2022,30:369-379. |
| APA | Ni, Ziyi,Xu, Jiaming,Wu, Yuwei,Li, Mengfan,Xu, Guizhi,&Xu, Bo.(2022).Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,30,369-379. |
| MLA | Ni, Ziyi,et al."Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 30(2022):369-379. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。