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.

中文翻译：

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使用脑电图进行纵向上肢运动功能评估的可行性

运动功能评估对于量化中风后的运动恢复至关重要。在康复领域，运动功能通常采用问卷调查的方式进行评估，这种评估并不完全客观，需要对检查者进行事先培训。一些研究小组报告说，脑电图（EEG）数据有可能成为运动功能的良好指标。然而，那些基于脑电图数据的运动功能评分并未在纵向范式中进行评估。脑电图数据的运动功能评分在长期临床应用中追踪运动功能变化的能力仍不清楚。为了研究在纵向范式中使用脑电图对运动功能进行评分的可行性，先前生成了卷积神经网络（CNN）脑电图模型和残差神经网络（ResNet）脑电图模型，以将脑电图数据转换为运动功能评分。为了验证中风后康复监测的应用，使用积极的 14 周康复计划中的初始小样本对预先建立的模型进行了评估。通过与评估会议中收集的上肢标准 Fugl-Meyer 评估 (FMA) 分数进行比较来评估 CNN 和 ResNet 的纵向性能。结果表明，两种网络均具有良好的准确性和鲁棒性（平均差值：CNN为1.22分，ResNet为1.03分），为该方法在长期临床应用中客观评估上肢运动功能提供了初步证据。