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EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation

Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner

TL;DR

The proposed classifier transfer approach enables motion prediction without explicit collection of training data and can be applied even with a small number of EEG channels, which speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.

Abstract

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.

EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation

TL;DR

The proposed classifier transfer approach enables motion prediction without explicit collection of training data and can be applied even with a small number of EEG channels, which speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.

Abstract

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.
Paper Structure (28 sections, 6 figures, 1 table)

This paper contains 28 sections, 6 figures, 1 table.

Table of Contents

  1. Background
  2. Methods
  3. Results
  4. Discussion
  5. Conclusion
  6. Declarations

Figures (6)

  • Figure 1: Experimental setup of the study. In a) a subject is shown sitting in front of a screen wearing an EEG-cap with $64$ electrodes. In b) the custom build experimental board including hand-switches (orange) and a button (blue) as well as the placed EMG-sensors (yellow) and motion tracking marker (green) are illustrated.
  • Figure 2: Custom selected channels from the left hemisphere. Channels used for the study are marked by a red circle.
  • Figure 3: Standard channel constellation based on the extended $10$-$20$ system. Channels used for this study are marked in green.
  • Figure 4: Illustration of the relabeling technique. In A) the determination of the label change point between two consecutive windows is shown while in B) the ground truth label after applying the method are illustrated.
  • Figure 5: Transfer effect: classification performance between both train-test conditions: (A) no transfer (unilateral-unilateral) and (C) transfer (bilateral-unilateral). Details for train-test conditions, see Table \ref{['tab:definition of train-test conditions']}. The n.s. stands for no significant difference
  • ...and 1 more figures