Contrast-Based Artifact Removal Enables Microstate Analysis in Ambulatory EEG

Recent advances in electroencephalography (EEG) technology present new opportunities for mobile neuroimaging and real-world human neuroscience studies. However, EEG is sensitive to many sources of artifacts, and it can be especially difficult to remove high amplitude motion artifacts.

We demonstrate a novel method using generalized eigen-decomposition (GED) for artifact removal, validated this approach using semi-simulated and real artifactual EEG collected during walking and jogging, and showed the feasibility of using cleaned ambulatory EEG data for brain microstate analysis.

We found that GED is effective even in ultra-low SNR (0.1 - 5) conditions, achieving a correlation of 0.93 and RMSE of 1.43 ${\bm{\mu V}}$ in recovering ground truth activity using semi-simulated data, and increased the number of brain components by 10.9 and 11.8 for real data. GED showed superior performance compared with artifact subspace reconstruction (ASR) and independent component analysis (ICA) methods on semi-simulated data in very low SNR regimes. Using cleaned data, we were able to extract canonical EEG microstates across all tasks and sessions for examining task-related modulation in microstate duration, occurrence, and time coverage. We observed increased duration, occurrence and time coverage of microstates A and duration of microstate B, and decreased occurrence and time coverage of microstate D during motion compared with rest, corresponding to heightened alertness and increased visual processing.

These findings not only validate our artifact removal approach but also open new avenues for investigating neural dynamics during naturalistic human behavior.