OBJECTIVE: To explore the impact of rehabilitation robot training (RRT) on upper limb motor function and daily activity ability in patients with stroke. METHODS: Forty patients meeting the inclusion criteria were randomly divided into the treatment group (TRE) and the control group (CON). Group TRE was trained with an upper limb rehabilitation robot and group CON was trained with traditional occupational therapy. The training time was six weeks, and the upper limb function and daily activities were then assessed. RESULTS: (1) There was no statistical significance in the Fugl-Meyer (FM) score, Wolf Motor Function Test (WMFT) score, and Modified Barthel Index (MBI) score between the two groups before treatment (P > 0.05). (2) After treatment, the FM score, WMFT score, and MBI score were significantly higher than before treatment (P < 0.01). (3) There was no significant significance between the two groups after treatment (P > 0.05). CONCLUSIONS: Both RRT and traditional occupational therapy training are useful for the recovery of upper limb motor function and daily life ability in the sub-acute stage of stroke.

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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.

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.