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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

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[Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on

Hao Song^1,2, Song Xu^3,4, Guoming Liu³

¹Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|December 27, 2022

Summary

This study presents an improved method to remove electrooculographic (EOG) artifacts from electroencephalography (EEG) signals for brain-computer interfaces (BCI). The technique effectively reduces artifacts while preserving crucial EEG data, enhancing BCI performance.

Keywords:

Electroencephalography Filter Independent component analysis Non-invasive brain computer interface Ocular artifact removal

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Context:

Non-invasive brain-computer interfaces (BCI) are increasingly researched for applications like mental disorder detection and physiological monitoring.
Electroencephalography (EEG) signals, crucial for BCI, are susceptible to contamination from electrooculographic (EOG) artifacts.
Artifacts significantly impair the accuracy and reliability of EEG signal analysis.

Purpose:

To develop an improved method for effectively removing electrooculographic (EOG) artifacts from electroencephalography (EEG) signals.
To enhance the signal-to-noise ratio in EEG data for non-invasive brain-computer interface (BCI) applications.
To minimize the loss of valuable EEG information during artifact removal.

Summary:

An improved independent component analysis (ICA) method combined with a frequency filter is proposed.
Artifact components are automatically identified using a dual threshold based on correlation coefficient and kurtosis.
The method leverages frequency differences between EOG and EEG to filter out EOG artifacts while retaining EEG data.

Impact:

The proposed method demonstrates effective EOG artifact removal, significantly improving EEG signal quality.
It minimizes the loss of essential EEG information, crucial for accurate BCI operation.
This advancement supports the broader adoption and efficacy of non-invasive BCI technologies.