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

Brain Machine Interfaces (BMIs) can help restore motor function to individuals with paralysis. These systems allow users to control an assistive device through the detection of movement-related brain activity. Such neural signatures are found through machine learning algorithms and training datasets that are generated from participants performing repetitive motor tasks. We anticipate that the movement-related brain waves of interest can 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 three open-access EEG datasets 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 epoch, 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 spectral power. In some cases, the ERDs were significantly different between earlier and later trials, and these differences led to changes in predicting the presence of movement from these ERDs. These results call for a reevaluation of BMI performance in datasets with numerous trials and an exploration of strategies that can compensate for longitudinal changes in movement-related brain activity used for BMIs.