Objective Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. Methods We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. Results The average online prediction time was 0.62 ± 0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. Conclusion Human voluntary movement can be predicted before movement occurs. Significance The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.

Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.

ABSTRACT:A simple, rapid, specific and reproducible reverse phase HPLC method was developed and validated for the simultaneous separation and estimation of the Nitazoxanide and Ofloxacin from the commercially available tablet dosage form. The chromatography was carried out using a combination of 0.863% (w/v) ammonium dihydrogen orthophosphate buffer and Acetonitrile (45:55 ratio (v/v)) at a flow rate of 1.0 ml/min and was monitored at 240 nm wavelength. The method was statistically validated by the study of linearity, accuracy, precision, limit of detection, limit of quantification, recovery and robustness. The retention time of Ofloxacin and Nitazoxanide were 2.099 + 0.010 and 5.623 + 0.03 minutes respectively. The calibration curve showed the excellent linearity over a concentration range of 3.125 μg to 0.5 mg/ml for Nitazoxanide and 1.25 μg to 0.2 mg/ml for Ofloxacin with correlation coefficients of 0.99999 and 0.99998 respectively. The proposed method can be used for the simultaneous estimation of Ofloxacin and Nitazoxanide in the combined dosages forms.

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