Abstract
Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.
Highlights
STROKE is the second most common cause of death and disability among all prevalent pathologies around the world [1]
The EEG functional connectivity and EEG power spectrum would be applied to our model as inputs to predict the recovery rate of FMU scores after two weeks
A dual-input Convolution Neural Network (CNN) prognosis model based on EEG signals has been developed to predict the patient’s recovery rate by EEG functional connectivity and EEG power spectrum, which is validated by the leave-one-out cross-validation
Summary
STROKE is the second most common cause of death and disability among all prevalent pathologies around the world [1]. 67% stroke survivors suffer from motor dysfunction and require efficient rehabilitation training [2,3,4]. Brain-computer interaction (BCI) training has been proven as a promising method in facilitating motor recovery [5,6]. Due to individual patients’ profile, the potential of recovery by using EEG based BCI can vary significantly. A robust prognostic method is required to predict recovery outcome for patients with various deficit characteristics. Matching patients with suitable therapy according to patients’ neuro-clinical profile becomes important for efficient rehabilitation
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