Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface*

Yang Bang-hua; He Liang-fei; Lin Lin; Wang Qian

doi:10.1631/FITEE.1400299

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Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface*

Computer & Automation | Updated：2022-06-06

- Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface*
- 脑机接口中基于约束独立分量分析和自适应滤波的眼电快速去除
- Frontiers of Information Technology & Electronic Engineering Vol. 16, Issue 6, Pages: 486-496(2015)
- Affiliations：
  
  Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
- Author bio：
  
  ‡Corresponding Author
  †E-mail:yangbanghua@126.comheliangfei126@126.comfxlinlin@shu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1400299
  CLC：
- Received：18 August 2014，
  
  Revised：2015-05-07，
  
  Accepted：13 January 2015，
  
  Published：2015-03
- Accepted：
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Yang Bang-hua, He Liang-fei, Lin Lin, et al. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 486-496.
DOI：

Yang Bang-hua, He Liang-fei, Lin Lin, et al. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 486-496. DOI： 10.1631/FITEE.1400299.

摘要

目的

眼电是脑电的主要干扰，采用眼电信号作为参考的自适应滤波能有效消除眼电干扰。然而眼电采集不方便且繁琐。在脑机接口中为从脑电中去除眼电，提出基于约束独立分量分析和自适应滤波的快速去除方法。该方法具有无需记录眼电信号且快速的优点。

创新点

所提方法避免了实验过程中直接对被试者进行眼电信号采集，减少被试者在实验过程中的不适。该方法处理后的识别正确率比单纯用传统ICA算法和不进行任何处理的源信号分别提高了3.3%和12.6%。另外，该方法的时间耗费较上述两种算法分别降低了83.5%和83.8%，更好地满足脑机接口在线要求。

方法

该方法分为两个阶段：第一阶段的目的是提取纯净的EOG信号。首先用ICA算法将输入信号分离成相互独立的分量（IC）。计算每个IC的峰态系数值并依据该值自动识别EOG独立分量（图2）。然后运用经验模态分解（EMD）将所识别的EOG信号自适应分解成数个IMF。根据IMF频域特征，选择数个IMF组合成纯净的EOG信号（图3）。第二阶段的目的是结合SCICA和RLS滤波算法去除混合在EEG信号中的EOG伪迹。首先SCICA利用第一阶段分离出的纯净EOG信号作为参考模板，迅速将混合在源信号中的EOG信号识别分离出。然后将该EOG信号分量作为RLS滤波器参考信号进行自适应滤波，最终去除EOG伪迹（图7）。

结论

针对脑机接口脑电信号包含的眼电伪迹，提出一种基于约束独立分量分析和自适应滤波的快速自动去除方法。该方法去除效果良好，可用于脑机接口中眼电的在线自动消除。

Abstract

Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference

but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs)

a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage

independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage

first

SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference

which avoids the need for parallel EOG recordings. In addition

the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects

the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG

respectively. In addition

the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS

respectively

which demonstrates a better and faster OA reduction.

关键词

Keywords

references

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