🤖 AI Summary
This study addresses the challenge of high false-positive rates and noise susceptibility in online asynchronous EEG-based motor intention detection, which undermines the reliability of rehabilitation robot triggering. For the first time, it systematically validates the efficacy of classifier ensembles in asynchronous EEG decoding by integrating SVM, MLP, and EEGNet into a unified ensemble model, complemented by a sliding-window post-processing mechanism. In both offline and pseudo-online evaluations, the proposed approach significantly outperforms the best individual classifier: it effectively suppresses early false alarms and achieves superior robustness and accuracy under reasonable window configurations, with particularly pronounced gains in simulated online scenarios.
📝 Abstract
Objective: Stroke is one of the leading causes of disabilities. 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. 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. 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.