Multi Models Fusion for Predicting Stroke Recovery Based on EEG Features

Predicting stroke recovery outcomes is crucial yet challenging, and different models yield varied predictions. For addressing the fusion of different models to provide more reliable prediction, a Dempster-Shafer Theory (DST)-based multi-model fusion method for different machine learning models is proposed in this paper to provide a robust prediction for the recovery outcomes. The Shannon entropy is applied as a measure of uncertainty and for performing DST fusion. It is well-suited for multi-model fusion, even though the output distribution of sub-models is unknown. The EEG-based Motor Imagery Brain-Computer Interface (MI-BCI) training is used in the experiments and validations on 13 stroke patients, with the recovery outcomes prediction accuracy of 92.3% and uncertainty of 0.24 of the multi-model fusion, demonstrating higher accuracy and lower uncertainty compared to any single prediction model.