EEG Channel Interpolation Using Deep Encoder-decoder Networks

Saba-Sadiya, Sari; Alhanai, Tuka; Liu, Taosheng; Ghassemi, Mohammad M.

doi:10.1109/bibm49941.2020.9312979

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…As previously mentioned, encoder-decoder based deep learning methods have proven useful for channel interpolation ( 13 ). In this section we discuss an extension of this approach that utilizes the same framework for artifact correction.…”

Section: Methodsmentioning

confidence: 99%

“…The channel interpolation model proposed in Saba-Sadiya et al ( 13 ) represented the EEG as a time series of 2D topologically organized arrays. This reflects the spatial nature of the EEG channel interpolation issue; the interpolated values at different time points are treated as independent.…”

Section: Methodsmentioning

confidence: 99%

“…However, research on convolutional neural networks for EEG decoding and visualization have shown performance benefits from presenting the input as a column of electrodes unfolding in time, as this facilitates the learning of temporal modulations ( 50 ). Since artifact correction is first and foremost a process of completing gaps across time we decided to depart from Saba-Sadiya et al ( 13 ) and use a 2D array representation with the number of time steps as the width of the array.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we only concern ourselves with a stacked convolutional auto-encoder. This architecture is shared by previously discussed state-of-the-art algorithms for both frame interpolation and channel interpolation ( 13 , 50 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, this type of additional information is rarely available and often requires special dedicated hardware. Recently, Sadiya et al proposed a deep learning convolutional auto-encoder based approach to learn task and subject-specific interpolation ( 13 ). By iteratively occluding channels in the input and using original data as the ground truth, the model learned how to interpolate channels in a self-supervised manner with no human annotation.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised EEG Artifact Detection and Correction

Saba-Sadiya

Alhanai

Liu

et al. 2021

Front. Digit. Health

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Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifact detection are deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifact correction or removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., “one-size-fits-all”). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble of unsupervised outlier detection algorithms to identify EEG artifacts that are unique to a given task and subject. The artifact segments are then passed to a deep encoder-decoder network for unsupervised artifact correction. We compared the performance of classification models trained with and without our method and observed a 10% relative improvement in performance when using our approach. Our method provides a flexible end-to-end unsupervised framework that can be applied to novel EEG data without the need for expert supervision and can be used for a variety of clinical decision tasks, including coma prognostication and degenerative illness detection. By making our method, code, and data publicly available, our work provides a tool that is of both immediate practical utility and may also serve as an important foundation for future efforts in this domain.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised EEG Artifact Detection and Correction

Saba-Sadiya

Alhanai

Liu

et al. 2021

Front. Digit. Health

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Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach

Ryabinin

Чуприна

Labutin

2021

Lecture Notes in Networks and Systems

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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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EEG Channel Interpolation Using Deep Encoder-decoder Networks

Cited by 11 publications

References 24 publications

Unsupervised EEG Artifact Detection and Correction

Unsupervised EEG Artifact Detection and Correction

Tackling IoT Interoperability Problems with Ontology-Driven Smart Approach

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

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