Unsupervised EEG Artifact Detection and Correction

Sari Saba-Sadiya; Eric Chantland; Tuka Alhanai; Taosheng Liu; Mohammad M Ghassemi

doi:10.3389/fdgth.2020.608920

Unsupervised EEG Artifact Detection and Correction

Front Digit Health. 2021 Jan 22:2:608920. doi: 10.3389/fdgth.2020.608920. eCollection 2020.

Authors

Sari Saba-Sadiya^{1

2}, Eric Chantland², Tuka Alhanai³, Taosheng Liu², Mohammad M Ghassemi¹

Affiliations

¹ Human Augmentation and Artificial Intelligence Lab, Department of Computer Science, Michigan State University, East Lansing, MI, United States.
² Neuroimaging of Perception and Attention Lab, Department of Psychology, Michigan State University, East Lansing, MI, United States.
³ Computer Human Intelligence Lab, Department of Electrical & Computer Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Abstract

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.

Keywords: artifact rejection; artifact removal; brain computer interface; electroencephalography; unsupervised learning.