Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data - Li, Xiangyun; Chen, Peng; Yu, Xi; Jiang, Ning (Frontiers in Aging Neuroscience, 2022)
Background: The aging of the world population poses a major health challenge, and brain-computer interface (BCI)technology has the potential to provide assistance and rehabilitation for the elderly. Objectives: The aim of this study was to investigate the electroencephalogram(EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks(CNN) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions. Methods: A total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time-frequency graphs and quantified event-related desynchronization(ERD) values. The fatigue level of the motor imagery was assessed by two indicators (θ+α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then rhythm entropy was introduced to analyze the complexity of cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by Convolutional Neural Networks and the accuracy was discussed based on the analysis results. Result: For the young and elderly, event-related desynchronization was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. The rhythm entropy of the frontal lobe showed a decreasing trend with the time during experiment for most of the young subjects, while there was an increasing tendency as the results for most older ones. Using the CNN classification method, the average classification accuracy of the elderly achieved around 70% which had close performance for the young adults by the same model. Conclusion: Compared with young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of MI-BCI in the elderly by considering the ERD and fatigue phenomenon together.