Characterization and Real-Time Removal of Motion Artifacts from EEG Signals
Accurate implementation of real-time neural interfaces requires handling major physiological and non-physiological artifacts that are associated with the measurement modalities. For example, electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible. In the present invention, we disclose a novel artifact mapping technique, with robustness properties, and its implementation to a non-linear mapping for the motion artifact problem. We then validate the proposed technique by asking subjects to walk on a treadmill at speeds from 1 to 4 mph to induce motion artifacts, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system with high spatial-temporal resolution. We also placed IMU sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait. The proposed technique allows characterization of the motion artifacts and provides a real-time compatible solution to filter them out from the EEG signals. We demonstrate the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-Related Spectral Perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies. Significance: The real-time compatibility of our filtering framework allows the effective use of noninvasive neural interfaces in applications such as closed-loop brain-machine interfaces (BMI), neuroimaging, neuromodulation, and neurostimulation to name a few. Importantly, the technique allows for a comprehensive adaptive filtering framework to increase the Signal to Noise Ratio (SNR) in physiological recordings in general.
  • 62/801,242

Case ID
Jose Contreras-Vidal
Professor, Electrical Engineering