The human brain can be considered as a graphical network having different regions with specific functionality and it can be said that a virtual functional connectivity are present between these regions. These regions are regarded as nodes and the functional links are regarded as the edges between them. The intensity of these functional links depend on the activation of the lobes while performing a specific task(e.g. motor imagery tasks, cognitive tasks and likewise). The main aim of this study is to understand the activation of the parts of the brain while performing three types of motor imagery tasks with the help of graph theory. Two indices of the graph, namely Network Density and Node Strength are calculated for 32 electrodes placed on the subject's head covering all the brain lobes and the nodes having higher intensity are identified.

Emotion is a complex set of interactions among subjective and objective factors governed by neural/hormonal systems resulting in the arousal of feelings and generate cognitive processes, activate physiological changes such as behavior. Emotion recognition can be correctly done by EEG signals. Electroencephalogram (EEG) is the direct reflection of the activities of hundreds and millions of neurons residing within the brain. Different emotion states create distinct EEG signals in different brain regions. Therefore EEG provides reliable technique to identify the underlying emotion information. This paper proposes a novel approach to recognize users emotions from electroencephalogram (EEG) signals. Audio signals are used as stimuli to elicit positive and negative emotions of subjects. For eight healthy subjects, EEG signals are acquired using seven channels of an EEG amplifier. The result reveal that frontal, temporal and parietal regions of the brain are relevant to positive emotion recognition and frontal and parietal regions are activated in case of negative emotion identification. After proper signal processing of the raw EEG, for the whole frequency bands the features are extracted from each channel of the EEG signals by Multifractral Detrended Fluctuation Analysis (MFDFA) method. We introduce an effective classifier named Support Vector Machine (SVM) to categorize the EEG feature space related to various emotional states into their respective classes. Next, we compare Support Vector Machine (SVM) with various other methods like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and K Nearest Neighbor (KNN). The average classification accuracy of SVM for positive emotions on the whole frequency bands is 84.50%, while the accuracy of QDA is 76.50% and with LDA 75.25% and KNN is only 69.625% whereas, for negative emotions it is 82.50%, while for QDA is 72.375% and with LDA 65.125% and KNN is only 70.50%.

Evolution has endowed human race with the most adroit brain, and to harness its potential to the fullest the concept of brain computer interface (BCI) has emerged. One of the most crucial components of BCI is the technique of brain imaging. The first approach in the field of brain imaging was to measure the electrical and magnetic activity of the brain, the techniques being known as Electroencephalography and Magnetoencephalography. Striving for furtherance, researchers came up with another alternative known as Magnetic Resonance Imaging. But it being confined to only structural imaging, the functional aspects of brain were mapped using functional magnetic resonance imaging. A similar but comparatively newer neuroimaging modality is Functional Near Infrared Spectroscopy. Transcranial Magnetic Stimulation neuro-physiological technique is based on the principle of electromagnetic induction. Based on nuclear medicine the brain imaging technologies that are widely explored in the world of BCI are Positron Emission Tomography and Single Positron Emission Tomography.

Abstract:Due to the rapid growth of internet and technology, protecting

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