Repetitive execution of a reach-and-lift task is associated with inter-trial changes in movement-related EEG features and their predictive power

Abstract

Brain Machine Interfaces (BMIs) are systems that can help restore motor function to individuals with paralysis. These systems utilize machine learning algorithms to detect how the user wants to control an assistive device from their brain activity. These algorithms require a training dataset to infer patterns between a user's brain waves and the control signal for the assistive device. These training datasets consist of brain activity recordings while participants perform a very repetitive motor task. We hypothesize that these training datasets can be compromised due to the high number of trials. We anticipate that the movement-related brain waves of interest attenuate over time due to neural efficiency, where the brain becomes more efficient with practice in a motor task. To explore this hypothesis, we used an open-access EEG dataset where participants performed a simple reach-and-lift task. From each trial, time windows associated with resting and movement periods were segmented. Alpha and beta-band spectral power was estimated for each of these epochs and event-related desynchronization (ERD) was estimated as the suppression in spectral power from rest to movement. These ERDs were compared between early and late trials in the dataset. We also used linear discriminant analysis to assess a machine learning algorithm's accuracy in classifying whether the time windows belonged to rest or movement based on the spectral power band. We found that earlier trials had stronger ERDs and larger classification accuracies, suggesting that the repetition in the motor task causes attenuation of movement-related brain wave patterns and reduces efficacy of the dataset. These results call for a reevaluation of BMI performance in datasets that have numerous trials and an exploration of strategies that can compensate for longitudinal changes in movement-related brain activity used for BMIs.

Decoding Electroencephalography Underlying Natural Grasp Tasks across Multiple Dimensions

“…The dynamic evolution of EEG signal power over time within a specific frequency band is assessed using event-related desynchronization (ERD) and event-related synchronization (ERS). The ERD(S) phenomenon is notably observed in the alpha (8)(9)(10)(11)(12)(13) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands during the execution of movements [14][15][16][17][18][19]. Moreover, functional brain connectivity reveals the process of information transmission among brain regions during movement.…”