A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation.

Abstract

Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.