EEG classifier cross-task transfer to avoid training sessions in
  robot-assisted rehabilitation
Niklas Kueper Su Kyoung Kim Elsa Andrea Kirchner
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.