Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

Bahador, Nooshin; Erikson, Kristo; Laurila, Jouko; Koskenkari, Juha; Ala‐Kokko, Tero; Kortelainen, Jukka

doi:10.1109/embc44109.2020.9175711

Cited by 5 publications

(2 citation statements)

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“…These artifacts with their particular distribution on specific channels create bad data in neighboring N. Bahador and J. Kortelainen are with physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland. channels [4,5] and may have spectral overlap with neurological activity of interest [6]. Therefore, these contaminated epochs are considered as bad epochs and totally removed from dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Besides faulty electrodes, indigenous sources may also cause contamination, being spatially distributed around their neighboring electrodes. These artifacts, with their particular distribution on specific channels, create bad data in neighboring channels [4,5] and may have spectral overlap with neurological activity of interest [6]. Therefore, these contaminated epochs are considered as bad epochs and totally removed from the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of missing channel in electroencephalogram using spatiotemporal correlation-based averaging

et al. 2021

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Objective. Electroencephalogram (EEG) recordings often contain large segments with missing signals due to poor electrode contact or other artifact contamination. Recovering missing values, contaminated segments and lost channels could be highly beneficial, especially for automatic classification algorithms, such as machine/deep learning models, whose performance relies heavily on high-quality data. The current study proposes a new method for recovering missing segments in EEG. Approach. In the proposed method, the reconstructed segment is estimated by substitution of the missing part of the signal with the normalized weighted sum of other channels. The weighting process is based on inter-channel correlation of the non-missing preceding and proceeding temporal windows. The algorithm was designed to be computationally efficient. Experimental data from patients (N = 20) undergoing general anesthesia due to elective surgery were used for the validation of the algorithm. The data were recorded using a portable EEG device with ten channels and a self-adhesive frontal electrode during induction of anesthesia with propofol from waking state until burst suppression level, containing lots of variation in both amplitude and frequency properties. The proposed imputation technique was compared with another simple-structure technique. Distance correlation (DC) was used as a measure of comparison evaluation. Main results.:The proposed method with average distance correlation of 82.48±10.01 (µ ± σ)% outperformed its competitor with average distance correlation of 67.89±14.12 (µ ± σ)% . This algorithm also showed better performance for an increasing number of missing channels. Significance. the proposed technique provides an easy-to-implement and computationally efficient approach for the reliable reconstruction of missing or contaminated EEG segments.

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Section: Introductionmentioning

confidence: 99%