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Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

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Unsupervised EEG Artifact Detection and Correction.

Sari Saba-Sadiya^1,2, Eric Chantland², Tuka Alhanai³

¹Human Augmentation and Artificial Intelligence Lab, Department of Computer Science, Michigan State University, East Lansing, MI, United States.

Frontiers in Digital Health

|October 29, 2021

Summary

This summary is machine-generated.

This study introduces a new Electroencephalography (EEG) noise-reduction method using representation learning for patient- and task-specific artifact detection and correction, improving classification performance by 10%. The unsupervised framework offers a flexible tool for clinical decision-making without expert supervision.

Keywords:

artifact rejection artifact removal brain computer interface electroencephalography unsupervised learning

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Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Electroencephalography (EEG) is crucial for diagnosing and monitoring neurological disorders.
EEG data is highly susceptible to physiological, movement, and equipment artifacts, complicating analysis.
Current artifact detection and correction methods are limited by manual annotation requirements and a one-size-fits-all approach.

Purpose of the Study:

To develop a novel, unsupervised EEG noise-reduction method for patient- and task-specific artifact detection and correction.
To improve the accuracy and efficiency of EEG data analysis in clinical settings.
To provide a flexible, end-to-end framework applicable to novel EEG data without expert supervision.

Main Methods:

Utilized representation learning to extract 58 clinically relevant features from EEG data.
Employed an ensemble of unsupervised outlier detection algorithms for artifact identification.
Applied a deep encoder-decoder network for unsupervised artifact correction.

Main Results:

Achieved a 10% relative improvement in classification model performance when using the proposed method.
Demonstrated effective patient- and task-specific artifact detection and correction.
The method operates in an unsupervised manner, eliminating the need for manual annotation.

Conclusions:

The developed method offers a flexible and unsupervised solution for EEG artifact removal.
This approach enhances the utility of EEG for various clinical applications, including coma prognostication and degenerative disease detection.
Publicly releasing the method, code, and data promotes further research and practical application in EEG analysis.