Induction and separation of motion artifacts in EEG data using a mobile phantom head device

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) is crucial for studying brain activity during movement.
• Head motion during EEG acquisition introduces significant artifacts, compromising signal quality.
• Effective methods for motion artifact removal in EEG are still under development.

Purpose of the Study:

• To introduce a novel phantom head and motion platform for evaluating EEG motion artifact removal.
• To assess the efficacy of signal processing techniques in mitigating motion-induced artifacts.
• To compare the sensitivity of different EEG acquisition systems to motion artifacts.

Main Methods:

• Utilized a phantom head with embedded dipolar sources to simulate human head electrical properties.
• Employed a custom motion platform to induce controlled sinusoidal vertical movements.
• Recorded EEG data using three acquisition systems under stationary and various motion conditions.

Main Results:

• Motion significantly degraded EEG signal-to-noise ratio (SNR) by up to 80% and increased power spectrum by up to 3600%.
• Independent Component Analysis (ICA) successfully isolated simulated brain sources across all tested conditions.
• ICA yielded a high correlation (r > 0.85) and a modest power spectrum increase (~15%) compared to stationary data.
• The SNR of ICA-activated components was 400%-700% higher than raw channel data SNR.

Conclusions:

• The developed phantom head and motion platform serve as a valuable tool for validating EEG artifact removal algorithms.
• ICA demonstrates effectiveness in isolating electrocortical signals and improving SNR in the presence of motion artifacts.
• This methodology facilitates objective comparisons of EEG systems regarding their susceptibility to motion artifacts.