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Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer

Xuesheng Qian^1,2, Mianjie Wang³, Xinyue Wang⁴

¹Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macao 999078, China.

Computational Intelligence and Neuroscience

|February 22, 2022

Summary

This summary is machine-generated.

Portable EEG technology (PEEGT) faces challenges with artifacts in natural settings. This study introduces a computer vision method to identify and annotate EEG artifacts from participant behavior in real-time, enabling more accurate neurotesting.

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Area of Science:

Neuroscience
Biomedical Engineering
Computer Vision

Background:

Portable EEG technology (PEEGT) offers potential for real-world neuroscience research.
Artifacts from subject activities in semi-constrained environments limit PEEGT's application.
Current artifact annotation methods hinder PEEGT's portability and cost-effectiveness.

Purpose of the Study:

To develop an intelligent method for real-time identification and annotation of EEG artifacts.
To address limitations in current postprocessing artifact removal techniques.
To enable large-scale neurotesting in natural environments outside the lab.

Main Methods:

Utilized computer vision (CV) to detect participant blinks and head movements, key sources of EEG artifacts.
Developed a real-time artifact annotation system based on recognized participant behaviors.
Shifted from signal-based postprocessing to behavior-based preprocessing for artifact management.

Main Results:

The CV-based method effectively identifies and annotates EEG artifact segments in real-time.
Comparative experiments validated the effectiveness of the CV method against manual annotation.
The approach lays the groundwork for accurate, real-time artifact removal in PEEGT.

Conclusions:

Computer vision offers a viable solution for real-time EEG artifact management in natural environments.
This method facilitates cost-effective, large-scale neurotesting without expensive lab equipment.
Enables wider adoption of PEEGT in real-world neuroscience applications.