Mind Over Machine: Using Action Observation to Reduce Mental Fatigue in Motor Imagery
摘要
Motor imagery (MI) based mental training has been proven to enhance motor skill learning in both healthy individuals and those with post-stroke hemiparesis. Mentally simulating movements without physically performing them, MI activates the same neural mechanisms that are activated during actual motor performance. The combination of MI and physical training has been shown to improve rehabilitation outcomes, highlighting its potential as a complementary strategy for motor recovery. However, after a prolonged MI, the person feels mentally fatigued affecting cognitive abilities. The efficacy of MI is impaired by mental fatigue (MF), making it challenging to effectively use it for neurorehabilitation. In this study, we investigated the hypothesis that combining action observation (AO) with MI can counteract MF. AO and MI use overlapping brain pathways for motor execution. Combining AO and MI (AO+MI) involves simultaneously observing an action and imagine performing it, enhancing motor representation and engagement. Our research focused on the effects of MF during pure MI and the combination of AO+MI. MF is characterized by an increase in parietal alpha power and a decrease in the N1 component of Event-Related Potential (ERP). Welch’s averaged modified periodogram method was used to estimate the power spectral density for all trials. Our results indicated an increase in the average power from the first to the last run of the experiment. Changes in the amplitude of the N1 ERP component and alpha-theta band power were observed in the topographic maps. Low-resolution electromagnetic tomography analysis was used to determine the maximum current density distribution. We found that AO+MI had the highest mean current density for N1 ERP compared to pure MI. In addition to the electrophysiological markers mentioned above, subjective assessment with the visual analogue scale and chalder fatigue scale were also used to assess mental fatigue. Our results showed that AO+ MI can maintain a better level of performance even in the presence of MF.