A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) is crucial for studying brain function in various environments.
• Artifacts in EEG data, such as ocular, myogenic, and movement-related, can compromise study results.
• Blind Source Separation (BSS) shows promise for artifact attenuation in high-density EEG (hdEEG).

Purpose of the Study:

• To develop and validate a novel multi-step BSS approach for optimizing artifact attenuation in hdEEG.
• To specifically address ocular, movement-related, and myogenic artifacts.
• To compare the efficacy of the proposed method against traditional single-step BSS approaches.

Main Methods:

• Developed a novel multi-step BSS strategy tailored for different artifact types in hdEEG.
• Collected hdEEG data from healthy participants in standing and walking conditions, including auditory stimulation.
• Quantified auditory event-related potentials (ERPs) and motor-related event-related desynchronization (ERD).

Main Results:

• The multi-step BSS approach resulted in significantly lower residual noise in hdEEG data.
• Stronger and more reliable modulations of neural activity were retrieved compared to single-step BSS.
• Confirmed that using specific BSS methods for different artifacts improves artifact removal performance.

Conclusions:

• The developed multi-step BSS approach offers superior artifact removal for hdEEG data.
• This technological solution enhances the utility of hdEEG for movement and rehabilitation research.
• Contributes to the advancement of mobile brain/body imaging applications.