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 R 2 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.

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Brain computer interface (BCI) has been widely studied to allow people to control external devices as an extension of capabilities or a replacement of lost functions. The decoding algorithm of brain signals is a crucial part in BCI, since its performance determines the efficiency of the interface. Decoding performance can be improved by generating optimal feature matrix. The objective of this paper is to propose and implement a decoding algorithm with optimized small dimension feature matrix on identifying motor intention of finger movement using electroencephalogram (EEG) signals. An experiment was designed and conducted, in which EEG was acquired from 10 healthy subjects during the left or the right index finger movement. Event-related desynchronization (ERD) topography was analyzed during motor tasks. A degree feature extraction algorithm was proposed based on the graph theory together with Support Vector Machine (SVM) to classify two kinds of index finger movement, which takes three factors into consideration: frequency bands, amplitude and range of ERD. The results showed that the algorithm can classify the finger movement effectively for 7 subjects based on a three-dimension optimized feature matrix, consisting of the maximum degree, average degree and clustering range. The proposed algorithm is not limited by the size of samples and can indicate the source area of the neural activities. Results also demonstrate that the proposed degree feature extraction algorithm can smooth signal noise and enlarge the feature differences between the contralateral and the ipsilateral hemispheres. INDEX TERMS Brain computer interfaces, event-related desynchronization topography, feature extraction, finger movement, graph theory.

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Various rehabilitation robots have been developed to assist the movement training of the upper limbs of stroke patients, among which some have been used to evaluate the motor recovery. However, how to understand the recovery of motor function from the quantitative assessment following robot-assisted rehabilitation training is still not clear. The objective of this study is to propose a quantitative assessment method of motor function based on the force and trajectory characteristics during robotic training to reflect motor functional recovery. To assist stroke patients who are not able to move voluntarily, an assistive training mode was developed for the robot-assisted rehabilitation system based on admittance control. Then, to validate the relationship between characteristic information and functional recovery, a clinical experiment was conducted, in which nine stroke patients and nine healthy subjects were recruited. The results showed a significant difference in movement range and movement smoothness during trajectory tracking tasks between stroke patients and healthy subjects. The two parameters above have a correlation with the Fugl-Meyer Assessment for Upper Extremity (FMU) of the involved patients. The multiple linear regression analysis showed FMU was positively correlated with parameters (R2=0.91,p<0.005). This finding indicated that the above-mentioned method can achieve quantitative assessment of motor function for stroke patients during robot-assisted rehabilitation training, which can contribute to promoting rehabilitation robots in clinical practice.