首页|Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

Automatic ocular artifact removal from EEG data using a hybrid CAE-RLS approach

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
  • 维普
Traditional methods for removing ocular artifacts (OAs) from electroencephalography (EEG) signals often involve a large number of EEG electrodes or require electrooculogram (EOG) as the reference,these constraints make subjects uncomfortable during the acquisition process and increase the complexity of brain-computer interfaces (BCI).To address these limitations,a method combining a convolutional autoencoder (CAE) and a recursive least squares (RLS) adaptive filter is proposed.The proposed method consists of offline and online stages.In the offline stage,the peak and local mean of the four-channel EOG signals are automatically extracted to obtain the CAE model.Once the model is trained,the EOG channels are no longer needed.In the online stage,by using the CAE model to identify the OAs from a single-channel raw EEG signal,the identified OAs and the given raw EEG signal are used as the reference and input for an RLS adaptive filter.Experiments show that the root mean square error (RMSE) of the CAE-RLS algorithm and independent component analysis (ICA) are 1.253 3 and 1.254 6respectively,and the power spectral density (PSD) curve for the CAE-RLS is similar to the original EEG signal.These experimental results indicate that by using only a couple of EEG channels,the proposed method can effectively remove OAs without parallel EOG records and accurately reconstruct the EEG signal.In addition,the processing time of the CAE-RLS is shorter than that of ICA,so the CAE-RLS algorithm is very suitable for BCI system.

electroencephalography (EEG)electrooculogram (EOG)ocular artifacts (OAs)recursive least squares (RLS)convolutional autoencoder (CAE)

Wang Zhongmin、Tian Meng、Liang Chen、Song Hui

展开 >

School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

This work was supported by the National Natural Science Foundation of ChinaScience and Technology Project in Shaanxi Province of ChinaGeneral Project in the Industrial Field of Shaanxi ProvinceSpecial Scientific Research Plan of the Education Department of Shaanxi Provinceand the Project of Xianyang Science and Technology Bureau

613731162019ZDLGY07-082018GY-01318JK06982017k01-25-1

2020

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(1)
  • 39