A Method for Improving Online Active Engagement During Lower Limb Rehabilitation Training Based on EEG Signals

Increasing online active engagement has been shown to effectively improve rehabilitation outcomes. This article introduces a method for improving online active engagement during lower limb rehabilitation training based on electroencephalogram (EEG) signals using an artificial neural network (ANN) recognition model as well as a virtual reality-based induced active engagement rehabilitation training system (VATS). The VATS consists of a bicycle-style rehabilitation instrument and a virtual reality game. Experiments were performed to train the ANN model and to validate the effectiveness of the VATS. We compared the accuracy of the ANN with that of k-nearest neighbor and support vector machine models, where active engagement results from eye tracker analysis were used as the gold standard. In addition, we used the surface electromyography engagement index (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E_r</i>) to compare the VATS with and without active engagement feedback. The results showed that the ANN model had the highest average accuracy of 76.95% and a maximum accuracy of 81.48%. A comparison of the ANN, KNN, and SVM models revealed statistically significant differences in accuracy (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.05). Significant differences were observed in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E_r</i> values between the VATS with and without active engagement feedback. The results indicated that the ANN has an apparent advantage in active engagement recognition. Moreover, the VATS was effective in improving active engagement. The proposed method utilized EEG signals to characterize active engagement intention and achieved real-time enhancement of active engagement through VATS. This method could be further used to improve the rehabilitation training effect in clinical applications.