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CLC number: TP399; R318.18

On-line Access: 2015-06-04

Received: 2014-08-18

Revision Accepted: 2015-01-13

Crosschecked: 2015-05-07

Cited: 5

Clicked: 4371

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Bang-hua Yang

http://orcid.org/0000-0003-4261-9875

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.6 P.486-496

http://doi.org/10.1631/FITEE.1400299


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


Author(s):  Bang-hua Yang, Liang-fei He, Lin Lin, Qian Wang

Affiliation(s):  Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Corresponding email(s):   yangbanghua@126.com, heliangfei126@126.com, fxlinlin@shu.edu.cn

Key Words:  Ocular artifacts, Electroencephalogram (EEG), Electrooculogram (EOG), Brain-computer interface (BCI), Spatial constraint independent component analysis based recursive least squares (SCICA-RLS)


Bang-hua Yang, Liang-fei He, Lin Lin, Qian Wang. 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.

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

The authors described a method named spatial constraint independent component analysis - recursive least squares (SCICA-RLS) to quickly remove ocular artifacts (OA) from EEG signals. The method shows 83.5% and 83.8% reduction in time-consumption compared to ICA and ICA-RLS methods. This is a very well written paper. The findings were interesting and have been presented clearly.

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

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。

关键词:眼电伪迹;脑电;眼电;脑机接口;约束独立分量分析和自适应滤波

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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