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The purpose of this study was to manufacture liquisolid compact of high dose poorly water-insoluble drug, Carbamazepine (CBZ) by using novel superdisintegrant for the purpose of fast disintegration and improved its dissolution rate. The solubility of CBZ was analyzed in various non-volatile solvents in order to find the vehicle with the maximum solubility. The dissolving profile of liquisolid compacts was compared to a marketed tablet formulation's dissolution profile. CBZ was found to be much more soluble in polyethylene glycol 200 than in the other solvents. Crosspovidone-containing formulations showed no disintegration, but all other formulations disintegrated after 91.2 seconds. A Starch Glutamate-Croscarmellose Sodium combination has a disintegration time of 42.5 seconds. The optimized batch NSC1 including Starch Glutamate-Croscarmellose Sodium had 94.81 % greater drug release compared to the marketed formulation. This investigation found that the novel superdisintegrant had the fastest disintegration and the highest drug release compared to other disintegrants.

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.