Automatic Eye-blink and Muscular Artifact Detection and Removal from EEG Signals using k-Nearest Neighbour Classifier and Long Short-Term Memory Networks

Electroencephalogram (EEG) is often corrupted with artifacts originating from sources like the eyes and muscles. Hybrid artifact removal methods often require human intervention for the adjustment of different parameters. We propose a robust method that can automatically detect and remove eye-blink...

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Bibliographic Details
Published in:IEEE sensors journal 2023-01, p.1-1
Main Authors: Ghosh, Rajdeep, Phadikar, Souvik, Deb, Nabamita, Sinha, Nidul, Das, Pranesh, Ghaderpour, Ebrahim
Format: Article
Language:English
Subjects:
Adaptive filters
Adaptive systems
Artifact removal
BCI
Electroencephalogram
Electroencephalography
Estimation
Eye-blink
Feature Extraction
Filtering
kNN
LSTM network
Muscles
Muscular artifact
Sensors
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Summary:Electroencephalogram (EEG) is often corrupted with artifacts originating from sources like the eyes and muscles. Hybrid artifact removal methods often require human intervention for the adjustment of different parameters. We propose a robust method that can automatically detect and remove eye-blink and muscular artifacts from EEG using k-nearest neighbor (kNN) classifier and a long short-term memory (LSTM) network. Our method adopts a sliding window of 0.5 s to detect and remove the artifacts from EEG. Features, such as the variance, peak-peak amplitude, and average rectified value are calculated for each EEG segment to identify corrupted segments using the kNN classifier. The kNN classifier detects the presence of artifacts, after which, the corresponding EEG window is forwarded to the LSTM network for artifact removal. The LSTM network is trained with the corrupted segments of 0.5 s as input and clean segments of 0.5 s as output. Our method achieved an accuracy of 97.4% in identifying corrupted EEG segments and an average correlation coefficient, structural similarity, signal to artifact ratio, and normalized mean squared error of 0.69, 0.76, 1.52 dB, and 0.0013, respectively, in cleaning the EEG. Our results outperformed other hybrid methods reported in the literature based on combination of ensemble empirical mode decomposition and canonical correlation analysis, combination of independent component analysis and wavelet decomposition, and tensor decomposition. The mean absolute error of our method is also better in comparison to other methods. Our method can be applied to single and multiple channels and does not require any tuning of parameters.
ISSN:1530-437X
DOI:10.1109/JSEN.2023.3237383