Background: For an individualized support of patients during rehabilitation,
learning of individual machine learning models from the human
electroencephalogram (EEG) is required. Our approach allows labeled training
data to be recorded without the need for a specific training session. For this,
the planned exoskeleton-assisted rehabilitation enables bilateral mirror
therapy, in which movement intentions can be inferred from the activity of the
unaffected arm. During this therapy, labeled EEG data can be collected to
enable movement predictions of only the affected arm of a patient. Methods: A
study was conducted with 8 healthy subjects and the performance of the
classifier transfer approach was evaluated. Each subject performed 3 runs of 40
self-intended unilateral and bilateral reaching movements toward a target while
EEG data was recorded from 64 channels. A support vector machine (SVM)
classifier was trained under both movement conditions to make predictions for
the same type of movement. Furthermore, the classifier was evaluated to predict
unilateral movements by only beeing trained on the data of the bilateral
movement condition. Results: The results show that the performance of the
classifier trained on selected EEG channels evoked by bilateral movement
intentions is not significantly reduced compared to a classifier trained
directly on EEG data including unilateral movement intentions. Moreover, the
results show that our approach also works with only 8 or even 4 channels.
Conclusion: It was shown that the proposed classifier transfer approach enables
motion prediction without explicit collection of training data. Since the
approach can be applied even with a small number of EEG channels, this speaks
for the feasibility of the approach in real therapy sessions with patients and
motivates further investigations with stroke patients.

该研究探讨了通过脑电图记录未受影响手臂的活动，以学习个体化机器学习模型来支持患者康复，结果表明预测运动的分类器在选定的受双边运动意图诱发的脑电图通道上训练时与全面训练脑电图数据的分类器在单边运动意图上的表现差异不显著。这一方法可在实际治疗中使用，与几个脑电图通道的数据训练有效，对于中风患者的进一步研究具有潜力。