Surrogate-Based Artifact Removal From Single-Channel EEG

Objective: the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminate...

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Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering Jg. 26; H. 3; S. 540 - 550
Hauptverfasser: Chavez, M., Grosselin, F., Bussalb, A., De Vico Fallani, F., Navarro-Sune, X.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2018
IEEE Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithm design and analysis
Algorithms
Artifact removal
Bioengineering
Brain-Computer Interfaces
Computer Science
Correlation
Databases, Factual
Electroencephalography
electroencephalography (EEG)
Electroencephalography - methods
Electroencephalography - statistics & numerical data
Electromyography
Eye Movements
Humans
Life Sciences
Monitoring
Movement
Muscle, Skeletal
Neurons and Cognition
Signal and Image Processing
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
single-channel EEG
surrogate data
Time series analysis
Wavelet Analysis
wavelet decomposition
Wavelet transforms
single-channel EEG
Artifact removal
electroencephalography
wavelet decomposition
surrogate data
EEG
ISSN:1534-4320, 1558-0210, 1558-0210
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Zusammenfassung:Objective: the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). Methods: by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG. Results: in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques. Significance: in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2018.2794184