A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collect...

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Detalles Bibliográficos
Autores principales: Dai, Yangyang, Duan, Feng, Feng, Fan, Sun, Zhe, Zhang, Yu, Caiafa, Cesar Federico, Marti Puig, Pere, Solé Casals, Jordi
Formato: Articulo
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Astronómicas
EEG
EMG artifact rejection
signal serialization
EEMD
CCA
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125406
https://www.mdpi.com/1099-4300/23/9/1170
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125406
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Astronómicas
EEG
EMG artifact rejection
signal serialization
EEMD
CCA
spellingShingle Ciencias Astronómicas
EEG
EMG artifact rejection
signal serialization
EEMD
CCA
Dai, Yangyang
Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, Cesar Federico
Marti Puig, Pere
Solé Casals, Jordi
A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
topic_facet Ciencias Astronómicas
EEG
EMG artifact rejection
signal serialization
EEMD
CCA
description An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
format Articulo
Articulo
author Dai, Yangyang
Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, Cesar Federico
Marti Puig, Pere
Solé Casals, Jordi
author_facet Dai, Yangyang
Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, Cesar Federico
Marti Puig, Pere
Solé Casals, Jordi
author_sort Dai, Yangyang
title A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
title_short A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
title_full A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
title_fullStr A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
title_full_unstemmed A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
title_sort fast approach to removing muscle artifacts for eeg with signal serialization based ensemble empirical mode decomposition
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/125406
https://www.mdpi.com/1099-4300/23/9/1170
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