Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada.
Sensors (Basel). 2020 Sep 25;20(19):5487. doi: 10.3390/s20195487.
Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl-Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.
运动功能评估对于量化中风后的运动功能恢复至关重要。在康复领域，运动功能通常使用基于问卷的评估方法进行评估，这些方法并不完全客观，并且需要检查者进行预先培训。一些研究小组报告称，脑电图 (EEG) 数据有可能成为运动功能的良好指标。然而，那些基于 EEG 数据的运动功能评分并未在纵向范式中进行评估。基于 EEG 数据的运动功能评分在长期临床应用中跟踪运动功能变化的能力尚不清楚。为了研究使用 EEG 在纵向范式中评分运动功能的可行性，先前生成了卷积神经网络 (CNN) EEG 模型和残差神经网络 (ResNet) EEG 模型，以将 EEG 数据转换为运动功能评分。为了验证在监测中风后康复中的应用，使用在积极的 14 周康复计划中的个体的初始小样本对预先建立的模型进行了评估。通过与在评估会议中收集的上肢标准 Fugl-Meyer 评估 (FMA) 分数进行比较，评估了 CNN 和 ResNet 的纵向性能。结果表明，这两种网络都具有良好的准确性和稳健性（CNN 的平均差异为 1.22 分，ResNet 的平均差异为 1.03 分），为该方法在长期临床应用中对手部运动功能的客观评估提供了初步证据。
