基于脑机接口快速序列呈现范式的模板在线更新算法研究*

doi:10.12267/j.issn.2096-5931.2026.06.001

信息通信技术与政策 ›› 2026, Vol. 52 ›› Issue (6): 2-8.doi: 10.12267/j.issn.2096-5931.2026.06.001

基于脑机接口快速序列呈现范式的模板在线更新算法研究^*

Research on online template update algorithm for brain-computer interface based on rapid serial visual presentation paradigm

余兆亮, 张洪欣, 杨晨

北京邮电大学电子工程学院, 北京 100876

收稿日期:2026-04-27 出版日期:2026-06-25 发布日期:2026-07-06
作者简介:
余兆亮,北京邮电大学电子工程学院硕士研究生在读,主要从事脑机接口、信号处理等方面的研究工作
张洪欣,北京邮电大学电子工程学院教授,主要从事电磁信息安全、生物电子、脑机接口等方面的研究工作
杨晨,北京邮电大学电子工程学院博士研究生在读,主要从事脑电信号分析、脑机接口系统开发等方面的研究工作
基金资助:
^*国家自然科学基金资助项目(62376035);^*国家自然科学基金资助项目(62071057)

YU Zhaoliang, ZHANG Hongxin, YANG Chen

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2026-04-27 Online:2026-06-25 Published:2026-07-06

摘要/Abstract

摘要：

为解决渐冻症患者在快速序列视觉呈现脑机接口应用中的信号非平稳问题,提出基于期望最大化的模板在线更新算法。该算法将脑电建模为模板与触发矩阵的组合,以最大似然估计同步更新模板。试验结果表明,融合在线更新算法和分层判别分量分析(Hierarchical Discriminant Component Analysis,HDCA)算法的模板在线更新算法(OU-HDCA算法)的分类准确率和信息传输速率均明显优于传统静态HDCA算法,并且呈现持续上升的趋势。该算法适配信号时变特性,提升了脑机接口应用系统的长期可靠性,可为临床辅助交流提供有效支撑。

关键词: 脑机接口, 脑电图, 在线更新, 信号估计

Abstract:

To tackle the non-stationarity of Electroencephalogram (EEG) signals in Rapid Serial Visual Presentation (RSVP) Brain-Computer Interfaces (BCIs) for patients with Amyotrophic Lateral Sclerosis (ALS), we propose an online template updating algorithm based on the Expectation-Maximization (EM) method. This algorithm formulates EEG signals as a combination of templates and trigger matrices, and updates the templates synchronously using maximum likelihood estimation. Experimental results show that the proposed Online-Updating Hierarchical Discriminant Component Analysis (OU-HDCA) algorithm, which combines the online updating scheme and Hierarchical Discriminant Component Analysis (HDCA), outperforms the traditional static HDCA algorithm remarkably in terms of classification accuracy and information transfer rate, with both indicators maintaining a continuous upward trend. The proposed algorithm adapts to the time-varying characteristics of EEG signals, improves the long-term reliability of BCI systems, and can serve as an effective solution for clinical assistive communication.

Key words: BCI, EEG, online update, signal estimation

中图分类号:

余兆亮, 张洪欣, 杨晨. 基于脑机接口快速序列呈现范式的模板在线更新算法研究^*[J]. 信息通信技术与政策, 2026, 52(6): 2-8.

YU Zhaoliang, ZHANG Hongxin, YANG Chen. Research on online template update algorithm for brain-computer interface based on rapid serial visual presentation paradigm[J]. Information and Communications Technology and Policy, 2026, 52(6): 2-8.

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http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2026.06.001

http://ictp.caict.ac.cn/CN/Y2026/V52/I6/2

图/表 3

参考文献 14

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基于脑机接口快速序列呈现范式的模板在线更新算法研究^*

Research on online template update algorithm for brain-computer interface based on rapid serial visual presentation paradigm

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