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 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 col...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
Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of a...Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.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.
基金Project supported by the National Natural Science Foundation of China(Nos.31100709 and 60975079)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the National Natural Science Foundation of China(No.61874171)the Alibaba Innovative Research Program of Alibaba Group。
文摘Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
