Contrast-Based Artifact Removal Enables Microstate Analysis in Ambulatory EEG

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Mobile electroencephalography (EEG) offers new possibilities for real-world neuroscience research.
• High-amplitude motion artifacts are a significant challenge in ambulatory EEG data acquisition.
• Existing artifact removal techniques struggle with low signal-to-noise ratio (SNR) conditions.

Purpose of the Study:

• To introduce and validate a novel generalized eigen decomposition (GED) method for removing motion artifacts from ambulatory EEG.
• To assess the performance of GED compared to established methods like artifact subspace reconstruction (ASR) and independent component analysis (ICA).
• To demonstrate the feasibility of using cleaned EEG data for brain microstate analysis during naturalistic activities.

Main Methods:

• Developed a generalized eigen decomposition (GED) algorithm for artifact removal in EEG signals.
• Validated the GED method using semi-simulated and real-world EEG data collected during walking and jogging.
• Performed brain microstate analysis on artifact-corrected EEG data.

Main Results:

• GED effectively removed motion artifacts, achieving high correlation (0.93) and low RMSE (1.43 μV) in semi-simulated data, even in ultra-low SNR (0.1-5) conditions.
• GED outperformed ASR and ICA in very low SNR regimes for artifact removal.
• Cleaned EEG data allowed for the extraction of canonical brain microstates, revealing modulations in microstate characteristics during motion compared to rest (e.g., increased duration/occurrence of microstates A and B, decreased for D).

Conclusions:

• The proposed GED method provides a robust approach for artifact removal in ambulatory EEG.
• This technique facilitates the investigation of neural dynamics during naturalistic human behaviors.
• The findings open new avenues for mobile neuroimaging and real-world neuroscience studies.