基于子空间方法的任务依赖脑电实时压缩算法研究*

doi:10.12267/j.issn.2096-5931.2024.05.004

信息通信技术与政策 ›› 2024, Vol. 50 ›› Issue (5): 26-33.doi: 10.12267/j.issn.2096-5931.2024.05.004

基于子空间方法的任务依赖脑电实时压缩算法研究^*

Research on task-dependent EEG real-time compression lgorithm based on the subspace method

王战阳, 张洪欣, 杨晨

北京邮电大学电子工程学院,北京 100876

收稿日期:2024-04-09 出版日期:2024-05-25 发布日期:2024-05-31
通讯作者: 杨晨,北京邮电大学电子工程学院讲师,长期从事脑电信号分析、脑机接口系统开发等方面的研究工作
作者简介:
王战阳,北京邮电大学电子工程学院硕士研究生在读,主要从事信号处理、脑机接口等方面的研究工作
张洪欣,北京邮电大学电子工程学院教授,长期从事电磁信息安全、生物电子、脑机接口等方面的研究工作
基金资助:
*国家自然科学基金项目(62376035);国家自然科学基金项目(62006024);国家自然科学基金项目(62071057);中央高校基本科研业务费专项资金项目(2023RC26)

WANG Zhanyang, ZHANG Hongxin, YANG Chen

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

Received:2024-04-09 Online:2024-05-25 Published:2024-05-31

摘要/Abstract

摘要：

现有研究提出的脑电信号的数据压缩算法虽然已经可以做到不错的压缩率,但是缺少对任务态数据的关注,同时在实时性上也难以满足脑机接口(Brain-Computer Interface,BCI)应用的要求,且会大幅度降低BCI系统的性能。基于子空间方法,在已知BCI系统任务态信息时,在压缩过程中尽可能保留任务相关脑电信号,可以在不影响BCI系统性能的同时大幅度减少需要传输的数据量。通过使用有限冲击响应滤波器组逼近任务相关成分的信号子空间,可以将脑电信号分割成压缩率允许的最小块,实时处理小块脑电信号。在与原数据在部分分类算法性能上无显著性差异的前提下,可提出一种仅传输8%数据量的算法,该算法不仅可以在传输较少数据量的同时较小地影响BCI系统的性能,且可做到实时压缩,具有重要的应用价值。

关键词: 脑机接口, 脑电, 信号压缩, 任务依赖

Abstract:

Although existing EEG compression algorithms can achieve good compression rates, they lack attention to task-related data and are also unable to meet the real-time requirements of brain-computer interface (BCI) applications, which will significantly reduce the performance of BCI systems. Based on the subspace method, when the task-related information of the BCI system is known, the task related EEG signals can be preserved as much as possible during the compression process, which can significantly reduce the amount of data that needs to be transmitted without affecting the performance of the BCI system. By using a finite impulse response filter bank to approximate the signal subspace of task related components, the EEG signal can be segmented into the smallest possible compression ratio and processed in real-time. On the premise that there is no significant difference in the performance of some classification algorithms compared to the original data, an algorithm that only transmits 8% of the data can be proposed. This algorithm can not only transmit less data while minimizing the impact on the performance of the BCI system, but also achieve real-time compression, whitch has important application value.

Key words: brain-computer interface, electroencephalography(EEG), signal compression, task dependence

中图分类号:

王战阳, 张洪欣, 杨晨. 基于子空间方法的任务依赖脑电实时压缩算法研究^*[J]. 信息通信技术与政策, 2024, 50(5): 26-33.

WANG Zhanyang, ZHANG Hongxin, YANG Chen. Research on task-dependent EEG real-time compression lgorithm based on the subspace method[J]. Information and Communications Technology and Policy, 2024, 50(5): 26-33.

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链接本文:

http://ictp.caict.ac.cn/CN/10.12267/j.issn.2096-5931.2024.05.004

http://ictp.caict.ac.cn/CN/Y2024/V50/I5/26

图/表 8

图1 非侵入式BCI系统流程图

图2 模拟在线系统子模块示意图

图3 CCA使用1 s数据分类结果

图4 FBCCA使用1 s数据分类结果

图5 CCA使用2 s数据分类结果

图6 FBCCA使用2 s数据分类结果

图7 CCA使用3 s数据分类结果

图8 FBCCA使用3 s数据分类结果

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基于子空间方法的任务依赖脑电实时压缩算法研究^*

Research on task-dependent EEG real-time compression lgorithm based on the subspace method

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