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Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition

BACKGROUND: Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked...

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Autores principales: Yeh, Chia-Lung, Chang, Hsiang-Chih, Wu, Chi-Hsun, Lee, Po-Lei
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910669/
https://www.ncbi.nlm.nih.gov/pubmed/20565751
http://dx.doi.org/10.1186/1475-925X-9-25
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author Yeh, Chia-Lung
Chang, Hsiang-Chih
Wu, Chi-Hsun
Lee, Po-Lei
author_facet Yeh, Chia-Lung
Chang, Hsiang-Chih
Wu, Chi-Hsun
Lee, Po-Lei
author_sort Yeh, Chia-Lung
collection PubMed
description BACKGROUND: Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased. METHODS: This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR). RESULTS: The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (P < 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis. CONCLUSIONS: The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.
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spelling pubmed-29106692010-07-28 Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition Yeh, Chia-Lung Chang, Hsiang-Chih Wu, Chi-Hsun Lee, Po-Lei Biomed Eng Online Research BACKGROUND: Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased. METHODS: This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR). RESULTS: The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (P < 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis. CONCLUSIONS: The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics. BioMed Central 2010-06-17 /pmc/articles/PMC2910669/ /pubmed/20565751 http://dx.doi.org/10.1186/1475-925X-9-25 Text en Copyright ©2010 Yeh et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Yeh, Chia-Lung
Chang, Hsiang-Chih
Wu, Chi-Hsun
Lee, Po-Lei
Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title_full Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title_fullStr Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title_full_unstemmed Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title_short Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition
title_sort extraction of single-trial cortical beta oscillatory activities in eeg signals using empirical mode decomposition
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2910669/
https://www.ncbi.nlm.nih.gov/pubmed/20565751
http://dx.doi.org/10.1186/1475-925X-9-25
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