Publication
Enhancing Robustness of Asynchronous EEG-Based Movement Prediction using Classifier Ensembles
Niklas Kueper; Kartik Chari; Elsa Andrea Kirchner
arxiv, 2026.
Abstract
Objective: Stroke is one of the leading causes of disabilities affecting the sensory and musculoskeletal system. One
promising approach is to extend the rehabilitation with self-initiated robot-assisted movement therapy. To enable
this, it is required to detect the patient’s intention to move to trigger the assistance of a robotic device. This
intention to move can be detected from human surface electroencephalography (EEG) signals; however, it is
particularly challenging to decode when classifications are performed online and asynchronously. In this work, the
effectiveness of classifier ensembles and a sliding-window postprocessing technique was investigated to enhance the
robustness of such asynchronous classification.
Approach: To investigate the effectiveness of classifier ensembles and a sliding-window postprocessing, two EEG
datasets with 14 healthy subjects who performed self-initiated arm movements were analyzed. Offline and
pseudo-online evaluations were conducted to compare ensemble combinations of the support vector machine
(SVM), multilayer perceptron (MLP), and EEGNet classification models.
Main results: The results of the pseudo-online evaluation show that the two model ensembles significantly
outperformed the best single model for the optimal number of postprocessing windows, as indicated by the number
[EEGNet3 vs. SVM-EEGNet2, p < 0.01; EEGNet3 vs. MLP-EEGNet2, p < 0.05]. In particular, for single models, an
increased number of postprocessing windows significantly improved classification performances. Interestingly, we
found no significant improvements between performances of the best single model and classifier ensembles in the
offline evaluation.
Significance: We demonstrated that classifier ensembles and appropriate postprocessing methods effectively enhance
the asynchronous detection of movement intentions from EEG signals. In particular, the classifier ensemble
approach yields greater improvements in online classification than in offline classification, and reduces false
detections, i.e., early false positives. As a result, our approach promises an improved applicability for the
asynchronous detection of EEG-based movement intentions in realistic out-of-the-lab applications.