Reconstruction of missing channel in EEG using spatiotemporal correlation-based averaging.

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Abstract

Electroencephalogram (EEG) recordings often contain large segments with missing signal 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. In the proposed method, the reconstructed segment is estimated by substitution of missing part of the signal with the normalized weighted sum of other channels. The weighting process is based on inter-channel correlation of 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. Data were recorded using a portable EEG device with 10 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. The distance correlation was used as a measure of comparison evaluation. 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. In conclusion, the proposed technique provides an easy-to-implement and computationally efficient approach for reliable reconstruction of missing or contaminated EEG segments.© 2021 IOP Publishing Ltd.