Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing metho...Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.展开更多
Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to r...Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.展开更多
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 interferenc...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 altifact 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.展开更多
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 ...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 6 respectively, 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.展开更多
传统盲源分离算法消除眼电伪迹须用到两个眼电信号作为参考,但在采集眼电信号时易给被试带来不适产生噪声,且识别时需要人为辨别,为了解决这些问题,提出一种基于FastICA的眼电伪迹自动去除方法。该方法先计算出FastICA提取出的各独立成...传统盲源分离算法消除眼电伪迹须用到两个眼电信号作为参考,但在采集眼电信号时易给被试带来不适产生噪声,且识别时需要人为辨别,为了解决这些问题,提出一种基于FastICA的眼电伪迹自动去除方法。该方法先计算出FastICA提取出的各独立成分与GFP(Global Field Power)值的相关系数,再比较相关系数,将其绝对值最大所对应的独立成分识别为眼电伪迹独立成分,最后把该独立成分置零重构干净的脑电信号,实现眼电伪迹的自动去除。通过自采的30例脑电数据实验结果表明:该方法能完全自动地去除眼电伪迹成分并有效保留其他脑电成分,且快速准确,适用于实时场合。展开更多
基金supported in part by the Science and Technology Major Project of Anhui Province(Grant No.17030901037)in part by the Humanities and Social Science Fund of Ministry of Education of China(Grant No.19YJAZH098)+2 种基金in part by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China(Grant Nos.GXXT-2020-012,GXXT-2021-044)in part by Science and Technology Planning Project of Wuhu City,Anhui Province,China(Grant No.2021jc1-2)part by Research Start-Up Fund for Introducing Talents from Anhui Polytechnic University(Grant No.2021YQQ066).
文摘Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.
文摘Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.
基金Project supported by the National Natural Science Foundation of China （Nos. 31100709 and 60975079） and the Shanghai Pujiang Program, China （No. 14PJ1431300）
文摘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 altifact 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.
基金supported by the National Natural Science Foundation of China (61373116)the Science and Technology Project in Shaanxi Province of China (2019ZDLGY07-08)+2 种基金the General Project in the Industrial Field of Shaanxi Province (2018GY-013)the Special Scientific Research Plan of the Education Department of Shaanxi Province (18JK0698)the Project of Xianyang Science and Technology Bureau (2017k01-25-1).
文摘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 6 respectively, 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.
文摘传统盲源分离算法消除眼电伪迹须用到两个眼电信号作为参考,但在采集眼电信号时易给被试带来不适产生噪声,且识别时需要人为辨别,为了解决这些问题,提出一种基于FastICA的眼电伪迹自动去除方法。该方法先计算出FastICA提取出的各独立成分与GFP(Global Field Power)值的相关系数,再比较相关系数,将其绝对值最大所对应的独立成分识别为眼电伪迹独立成分,最后把该独立成分置零重构干净的脑电信号,实现眼电伪迹的自动去除。通过自采的30例脑电数据实验结果表明:该方法能完全自动地去除眼电伪迹成分并有效保留其他脑电成分,且快速准确,适用于实时场合。
