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Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.

Among the different techniques used for the analysis of electroencephalograms, the phase lag index has become an important method for the calculation of the functional brain connectivity. Currently, this method is implemented offline due to its high computational complexity restricting it from real-time applications that would require an online neurofeedback. In this paper, we propose a new architecture to calculate the phase lag index of electroencephalograms in real-time. As a proof of concept, a 32 bit and 16-channel system running at 188.32 MHz was synthesized on a Stratix IV GX FPGA. The system was tested and the simulations demonstrated that the system could perform the calculation of the Phase lag index at least 66 times faster than the MATLAB software with a mean square error of less than 5.72×10 -6 .

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

In this paper, we have developed a new measure of understanding the temporal evolution of phase synchronization for EEG signals using cross-electrode information. From this measure it is found that there exists a small number of well-defined phase-synchronized states, each of which is stable for few milliseconds during the execution of a face perception task. We termed these quasi-stable states as synchrostates. We used k-means clustering algorithms to estimate the optimal number of synchrostates from 100 trials of EEG signals over 128 channels. Our results show that these synchrostates exist consistently in all the different trials. It is also found that from the onset of the stimulus, switching between these synchrostates results in well-behaved temporal sequence with repeatability which may be indicative of the dynamics of the cognitive process underlying that task. Therefore these synchrostates and their temporal switching sequences may be used as a new measure of the stability of phase synchrony and information exchange between different regions of a human brain.

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.

In this paper we propose a novel channel identification scheme for solving permutation indeterminacy introduced by Independent Component Analysis (ICA) for artifacts removal from recorded three channel ECG signals within the remote health monitoring environment. The proposed scheme does not depend on the definition of any specific artifact which is the case with the existing approach and therefore leads to more robust and generic solution to this problem. The proposed scheme has been validated using nine practical case studies and its robustness has been proved by comparison with the existing approach. Simulation results show that the proposed scheme works successfully for all the nine cases whereas the existing approach fails to identify the correct channel for four cases.