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Their performances are also compared with other clustering algorithms using evaluation criteria such as SSE, entropy and purity. The results have revealed the good performance of the proposed parameter-independent clustering techniques and also shown that most of the biomedical datasets in the experiments demonstrated their tendency towards convex-dominated data point sets.Fugl-Meyer assessment is an accepted method of evaluating motor function for people with stroke. A challenge associated with this assessment is the availability of trained examiners to carry out the evaluation. Neurophysiological biomarkers show promise in addressing the above impediment. Our study investigated the potential of using resting state electroencephalographic (EEG) functional connectivity measures as biomarkers for estimating Fugl-Meyer upper extremity motor score (FMU) in people with chronic stroke. Resting state EEG was recorded from 10 individuals with stroke. Functional connectivity was evaluated through five different processing algorithms and quantified in terms of maximum-coherence between EEG electrodes at 15 frequencies from 1 to 45 Hz. We applied a multi-variate Partial Least Squares (PLS) Correlation analysis to simultaneously identify specific connectivity channels (EEG electrode pairings) and frequencies that robustly correlated with FMU. CNO agonist cell line We then applied PLS-Regression to the identified channels and frequencies to generate a set of coefficients for estimating the FMU. Participants were randomly assigned to a training-set of eight and a test-set of two. Crossvalidation with leave-one-out approach on the training-set, using Phase-Lag-Index processing algorithm, resulted in an R2 of 0.97 and a least-square linear fit slope of 1 for predicted versus actual FMU, with a root-mean-square error of 1.9 on FMU scale. Application of regression coefficients to the connectivity measures from the test-set resulted in predicted FMU of 47 and 38 versus actual scores of 46 and 39, respectively. Our results demonstrated that the evaluation of neural correlates of FMU shows promise in addressing the challenges associated with the availability of trained examiners to carry out the assessments.Functional assessment is an essential part of rehabilitation protocols after stroke. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. In order to objectively quantify the upper-limb motor impairments in patients with post-stroke hemiparesis, this study proposes a novel assessment approach based on motor synergy quantification and multi-modality fusion. Fifteen post-stroke hemiparetic patients and fifteen age-matched healthy persons participated in this study. During different goal-directed tasks, kinematic data and surface electromyography (sEMG) signals were synchronously collected from these participants, and then motor features extracted from each modal data can be fed into the respective local classifiers. In addition, kinematic synergies and muscle synergies were quantified by principal component analysis (PCA) and k weighted angular similarity (kWAS) algorithm to provide in-depth analysis of the coactivated features responsible for observable d a significant correlation with the score of standard clinical tests (R= -0.87, P=1.98e-5). These promising results show the feasibility of applying the proposed method to clinical assessments for post-stroke hemiparetic patients.Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that can influence cortical excitability. Low-frequency rTMS (stimulation frequency.1Hz) can induce inhibitory effects on cortical excitability. In order to investigate dynamic changes in neuronal activity after low-frequency rTMS, 20 healthy subjects received 1-Hz rTMS over the right motor area, and electroencephalography (EEG) in resting condition with eyes open was recorded before rTMS and at 0 min, 20 min, 40 min, and 60 min after rTMS. Power values, functional connectivity based on a weighted phase lag index (wPLI), and network characteristics were assessed and compared to study the aftereffects of rTMS. Our results show that low-frequency rTMS produced a delayed long-lasting increase in alpha-band power values in frontoparietal brain areas and an immediate long-lasting increase in theta-band power values in the ipsilateral frontal and contralateral centroparietal areas. In the alpha band, functional connectivity decreased immediately after rTMS but significantly increased at 20 min after rTMS. Moreover, an analysis of undirected graphs revealed that the number of connections significantly changed in the anterior and posterior regions in the alpha band. In addition, there were significant decreases in clustering coefficients of the channels near the site of stimulation in the alpha and theta bands after rTMS. In conclusion, low-frequency rTMS produces widespread and long-lasting alterations in neural oscillation and functional connectivity. This work implies that low-frequency rTMS can induce inhibitory effects on motor cortical excitability ipsilateral to the stimulation site.Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network’s generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of 0.1dB, whether trained on or not.