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Brain-computer interface (BCI) connects the outside world, in real time and in a natural way, like biological communication system. It facilitates the communication link from the brain to the external world by converting brain thoughts in to control commands to control the external devices, such as wheelchair, keyboard mouse, and other home appliances. Measuring the electrical brain activity by placing electrodes over scalp is called electroencephalogram (EEG). By combining these two techniques, we are able to create EEG-based BCI. In this paper, we use band power and radial basis function to analyze the signal for four mentally composed tasks to design four states BCI for a neurodegenerative person using EEG. Online study was conducted to analyze the performance of the wheelchair for a neurodegenerative person. The result shows that an overall average classification accuracy of 92.50% and individual tasks with an average classification of 95%, 87.50%, 92.50%, and 95.00% were achieved for the four tasks. The result proves that control commands generated from the EEG signal have the bcapacity to control the intelligent systems.INDEX TERMS Brain computer interface, band power, radial basis function, FRDM-KL25Z.

Compression ignition engines are widely used due to their lower energy consumption and enhanced combustion efficiency. In this experimental investigation, the feasibility of fuelling a single cylinder 4 stroke direct injection compression ignition engine with methyl esters of palmkernel (PME) oil along with various fractions of aluminium oxide nano particles (ANOP) were analysed. Two stage transesterification process was adopted to prepare PME. PME20 blend was formulated and fused using high speed homogenizer with varying proportions of AONP as 25 ppm, 50 ppm and 100 ppm in the presence of hexadecyl trimethyl ammonium bromide as surfactant. The experimental investigations were conducted at rated power of 3.5kW at 1500rpm. It was noticed that supplementation of AONP affected the ignition delay significantly favouring enhanced combustion efficiency. The rate of heat release and in-cylinder pressure was substantially increased with notable reduction in ignition delay. Addition of AONP showed an increase in brake thermal efficiency and exhaust gas temperature with diminution in brake specific energy consumption. The unburned hydrocarbons, carbon monoxide and smoke density decreased sharply with an upsurge in NOx. Increase in AONP concentration up-to 100 ppm with PME20 was found to give better combustion and performance characteristics.

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 -w, T2 -w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C -Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images.

The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by feature extraction and classification of features at different frequency levels for Beta, Alpha, Theta and Delta waves. The Principal Component Analysis(PCA ),and the Wavelet Transform(WT) can be used for dimensionality reduction and feature extraction . The Artificial Neural Network (ANN) which is a computationally powerful model, is used as the classifier. The paper presents the comparison between the two approaches PCA and WT applied on the ANN Classifier.

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