Search results for: Brain drain

Authors: Muhibul Haque Bhuyan

Abstract:

This paper reports on the impact study with the variation of the gate insulation material and thickness on different models of pocket implanted sub-100 nm n-MOS device. The gate materials used here are silicon dioxide (SiO₂), aluminum silicate (Al₂SiO₅), silicon nitride (Si₃N₄), alumina (Al₂O₃), hafnium silicate (HfSiO₄), tantalum pentoxide (Ta₂O₅), hafnium dioxide (HfO₂), zirconium dioxide (ZrO₂), and lanthanum oxide (La₂O₃) upon a p-type silicon substrate material. The gate insulation thickness was varied from 2.0 nm to 3.5 nm for a 50 nm channel length pocket implanted n-MOSFET. There are several models available for this device. We have studied and simulated threshold voltage model incorporating drain and substrate bias effects, surface potential, inversion layer charge, pinch-off voltage, effective electric field, inversion layer mobility, and subthreshold drain current models based on two linear symmetric pocket doping profiles. We have changed the values of the two parameters, viz. gate insulation material and thickness gradually fixing the other parameter at their typical values. Then we compared and analyzed the simulation results. This study would be helpful for the nano-scaled MOS device designers for various applications to predict the device behavior.

Keywords: Linear symmetric pocket profile, pocket implanted n-MOS Device, model, impact of gate material, insulator thickness.

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Authors: M.Victoria Garcia-Camba, M.Isabel Garcia-Planas

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The use of brain stem auditory evoked potential (BAEP) is a common way to study the hearing function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and the authors best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to consider both ears; with these latest data, it has been possible to diagnose more precisely some cases than with the previous data had been diagnosed as “normal”despite showing signs of some alteration that motivated the new consultation to the specialist.

Keywords: Ear, neurodevelopment, auditory evoked potentials, intervals of normality, learning disabilities.

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Authors: Jiewei Li, Hongyan Cui, Chunqi Chang, Yong Hu

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It was expected to benefit patient with hemiparesis after stroke by extensive arm rehabilitation, to partially regain forearm and hand function. This paper propose a robotic rehabilitation arm in assisting the hemiparetic patient to learn new ways of using and moving their weak arms. In this study, the robotic arm was driven by a somatosensory stimulated brain computer interface (BCI), which is a new modality BCI. The use of somatosensory stimulation is not only an input for BCI, but also a electrical stimulation for treatment of hemiparesis to strengthen the arm and improve its range of motion. A trial of this robotic rehabilitation arm was performed in a stroke patient with pure motor hemiparesis. The initial trial showed a promising result from the patient with great motivation and function improvement. It suggests that robotic rehabilitation arm driven by somatosensory BCI can enhance the rehabilitation performance and progress for hemiparetic patients after stroke.

Keywords: Robotic rehabilitation arm, brain computer interface (BCI), hemiparesis, stroke, somatosensory stimulation.

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Authors: Mikel Alonso López, María Francisca Blasco López, Víctor Molero Ayala

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The present study explains the somatic marker theory of Antonio Damasio, which indicates that when making a decision, the stored or possible future scenarios (future memory) images allow people to feel for a moment what would happen when they make a choice, and how this is emotionally marked. This process can be conscious or unconscious. The development of new Neuromarketing techniques such as functional magnetic resonance imaging (fMRI), carries a greater understanding of how the brain functions and consumer behavior. In the results observed in different studies using fMRI, the evidence suggests that the somatic marker and future memories influence the decision-making process, adding a positive or negative emotional component to the options. This would mean that all decisions would involve a present emotional component, with a rational cost-benefit analysis that can be performed later.

Keywords: Emotions, decision making, somatic marker, consumer´s brain.

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Authors: M. Sadeghi, A.H. Jafari, S.M.P. Firoozabadi

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Deep Brain Stimulation or DBS is the second solution for Parkinson's Disease. Its three parameters are: frequency, pulse width and voltage. They must be optimized to achieve successful treatment. Nowadays it is done clinically by neurologists and there is not certain numerical method to detect them. The aim of this research is to introduce simulation and modeling of Parkinson's Disease treatment as a computational procedure to select optimum voltage. We recorded finger tremor signals of some Parkinsonian patients under DBS treatment at constant frequency and pulse width but variable voltages; then, we adapted a new model to fit these data. The optimum voltages obtained by data fitting results were the same as neurologists- commented voltages, which means modeling can be used as an engineering method to select optimum stimulation voltages.

Keywords: modeling, Deep Brain Stimulation, Parkinson'sdisease, tremor.

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Authors: Sergio Pissanetzky

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The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.

Keywords: AI, artificial intelligence, complex system, object oriented, OO, refactoring.

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Authors: Benjamin Y. M. Kwan, Hon Keung Kwan

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Noise contamination in a magnetic resonance (MR) image could occur during acquisition, storage, and transmission in which effective filtering is required to avoid repeating the MR procedure. In this paper, an iterative asymmetrical triangle fuzzy filter with moving average center (ATMAVi filter) is used to reduce different levels of salt and pepper noise in a brain MR image. Besides visual inspection on filtered images, the mean squared error (MSE) is used as an objective measurement. When compared with the median filter, simulation results indicate that the ATMAVi filter is effective especially for filtering a higher level noise (such as noise density = 0.45) using a smaller window size (such as 3x3) when operated iteratively or using a larger window size (such as 5x5) when operated non-iteratively.

Keywords: Brain images, Fuzzy filters, Magnetic resonance imaging, Salt and pepper noise reduction.

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Authors: M. Chojak

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Recently, more and more scientific disciplines refer to research in the field of neurobiology. Interdisciplinary research procedures are created using modern methods of brain imaging. Neither did the pedagogues start looking for neuronal conditions for various processes. The publications began to show concepts such as ‘neuropedagogy’, ‘neuroeducation’, ‘neurodidactics’, ‘brain-friendly education’. They were and are still used interchangeably. In the offer of training for teachers, the topics of multiple intelligences or educational kinesiology began to be more and more popular. These and other ideas have been actively introduced into the curricula. To our best knowledge, the literature on the subject lacks articles organizing the new nomenclature and indicating the methodological framework for research that would confirm the effectiveness of the above-mentioned innovations. The author of this article tries to find the place for neuropedagogy in the system of sciences, define its subject of research, methodological framework and basic concepts. This is necessary to plan studies that will verify the so-called neuromyths.

Keywords: Brain, education, neuropedagogy, research.

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Authors: Hamed Alizadeh Dashagholi, Hossein Yousefi-Banaem, Mina Naeimi

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Current study established for EEG signal analysis in patients with language disorder. Language disorder can be defined as meaningful delay in the use or understanding of spoken or written language. The disorder can include the content or meaning of language, its form, or its use. Here we applied Z-score, power spectrum, and coherence methods to discriminate the language disorder data from healthy ones. Power spectrum of each channel in alpha, beta, gamma, delta, and theta frequency bands was measured. In addition, intra hemispheric Z-score obtained by scoring algorithm. Obtained results showed high Z-score and power spectrum in posterior regions. Therefore, we can conclude that peoples with language disorder have high brain activity in frontal region of brain in comparison with healthy peoples. Results showed that high coherence correlates with irregularities in the ERP and is often found during complex task, whereas low coherence is often found in pathological conditions. The results of the Z-score analysis of the brain dynamics showed higher Z-score peak frequency in delta, theta and beta sub bands of Language Disorder patients. In this analysis there were activity signs in both hemispheres and the left-dominant hemisphere was more active than the right.

Keywords: EEG, electroencephalography, coherence methods, language disorder, power spectrum, z-score.

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Authors: Rima Hleiss, Elie Bitar, Mahmoud Hassan, Mohamad Khalil

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A comprehensive study of object recognition in the human brain requires combining both spatial and temporal analysis of brain activity. Here, we are mainly interested in three issues: the time perception of visual objects, the ability of discrimination between two particular categories (objects vs. animals), and the possibility to identify a particular spatial representation of visual objects. Our experiment consisted of acquiring dense electroencephalographic (EEG) signals during a picture-naming task comprising a set of objects and animals’ images. These EEG responses were recorded from nine participants. In order to determine the time perception of the presented visual stimulus, we analyzed the Event Related Potentials (ERPs) derived from the recorded EEG signals. The analysis of these signals showed that the brain perceives animals and objects with different time instants. Concerning the discrimination of the two categories, the support vector machine (SVM) was applied on the instantaneous EEG (excellent temporal resolution: on the order of millisecond) to categorize the visual stimuli into two different classes. The spatial differences between the evoked responses of the two categories were also investigated. The results showed a variation of the neural activity with the properties of the visual input. Results showed also the existence of a spatial pattern of electrodes over particular regions of the scalp in correspondence to their responses to the visual inputs.

Keywords: Brain activity, dense EEG, evoked responses, spatiotemporal analysis, SVM, perception.

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Authors: Ratnakar Tripathi, Rajnikant Mishra

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The PAX6, a transcription factor, is essential for the morphogenesis of the eyes, brain, pituitary and pancreatic islets. In rodents, the loss of Pax6 function leads to central nervous system defects, anophthalmia, and nasal hypoplasia. The haplo-insufficiency of Pax6 causes microphthalmia, aggression and other behavioral abnormalities. It is also required in brain patterning and neuronal plasticity. In human, heterozygous mutation of Pax6 causes loss of iris [aniridia], mental retardation and glucose intolerance. The 3- deletion in Pax6 leads to autism and aniridia. The phenotypes are variable in peneterance and expressivity. However, mechanism of function and interaction of PAX6 with other proteins during development and associated disease are not clear. It is intended to explore interactors of PAX6 to elucidated biology of PAX6 function in the tissues where it is expressed and also in the central regulatory pathway. This report describes In-silico approaches to explore interacting proteins of PAX6. The models show several possible proteins interacting with PAX6 like MITF, SIX3, SOX2, SOX3, IPO13, TRIM, and OGT. Since the Pax6 is a critical transcriptional regulator and master control gene of eye and brain development it might be interacting with other protein involved in morphogenesis [TGIF, TGF, Ras etc]. It is also presumed that matricelluar proteins [SPARC, thrombospondin-1 and osteonectin etc] are likely to interact during transport and processing of PAX6 and are somewhere its cascade. The proteins involved in cell survival and cell proliferation can also not be ignored.

Keywords: Interacting Proteins, Pax6, PIP, STRING

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotiv EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: Brain Computer Interface (BCI), Electroencephalogram (EEG), EEGLab, BCILab, Emotiv, Emotions, Interval features, Spectral features, Artificial Neural Network, Control applications.

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Authors: B. Thiagarajan, R. Bremananth

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Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and treatments. Brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Locating the tumor within MR (magnetic resonance) image of brain is integral part of the treatment of brain tumor. This segmentation task requires classification of each voxel as either tumor or non-tumor, based on the description of the voxel under consideration. Many studies are going on in the medical field using Markov Random Fields (MRF) in segmentation of MR images. Even though the segmentation process is better, computing the probability and estimation of parameters is difficult. In order to overcome the aforementioned issues, Conditional Random Field (CRF) is used in this paper for segmentation, along with the modified artificial bee colony optimization and modified fuzzy possibility c-means (MFPCM) algorithm. This work is mainly focused to reduce the computational complexities, which are found in existing methods and aimed at getting higher accuracy. The efficiency of this work is evaluated using the parameters such as region non-uniformity, correlation and computation time. The experimental results are compared with the existing methods such as MRF with improved Genetic Algorithm (GA) and MRF-Artificial Bee Colony (MRF-ABC) algorithm.

Keywords: Conditional random field, Magnetic resonance, Markov random field, Modified artificial bee colony.

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Authors: Kartika S. Hamdan, Zainiharyati M. Zain, Mohamed I. A. Halim, Jafri M. Abdullah, Robert D. O'Neill

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This work attempts to improve the permselectivity of poly-ortho-phenylenediamine (PPD) coating for glutamate biosensor applications on Pt microelectrode, using constant potential amperometry and cyclic voltammetry. Percentage permeability of the modified PPD microelectrode was carried out towards hydrogen peroxide (H2O2) and ascorbic acid (AA) whereas permselectivity represents the percentage interference by AA in H2O2 detection. The 50-μm diameter Pt disk microelectrode showed a good permeability value toward H2O2 (95%) and selectivity against AA (0.01%) compared to other sizes of electrode studied here. The electrode was further modified with glutamate oxidase (GluOx) that was immobilized and cross linked with glutaraldehyde (GA, 0.125%), resulting in Pt/PPD/GluOx-GA electrode design. The maximum current density Jmax and apparent Michaelis constant, KM, obtained on Pt/PPD/GluOx-GA electrodes were 48 μA cm-2 and 50 μM, respectively. The linear region slope (LRS) was 0.96 μA cm-2 mM-1. The detection limit (LOD) for glutamate was 3.0 ± 0.6 μM. This study shows a promising glutamate microbiosensor for brain glutamate detection. 

Keywords: Brain, Glutamate, Microbiosensor.

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Authors: Seokbeen Lim, Gilwon Yoon

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The readiness potential in brain waves is a brain activity related with an intention whose potential arises even before its conscious intention. This study was carried out in order to understand the generation and mechanism of the readiness potential more. The experiment with two subjects was conducted in two ways following the Oddball task protocol. Firstly, auditory stimuli were randomly presented to the subjects. The subject was allowed to press the keyboard with the right index finger only when the subject heard the target stimulus but not the standard stimulus. Secondly, unlike the first one, the auditory stimuli were randomly presented, and the subjects pressed the keyboard in the same manner, but at the same time with grasping action of the left hand. The readiness potential showed up for both of these experiments. In the first Oddball experiment, the readiness potential was detected only when the target stimulus was presented. However, in the second Oddball experiment with the left hand action of grasping something, the readiness potential was detected at the presentation of for both standard and target stimuli. However, detected readiness potentials with the target stimuli were larger than those of the standard stimuli. We found an interesting phenomenon that the readiness potential was able to be detected even the standard stimulus. This indicates that motor-related readiness potentials can be generated only by the intention to move. These results present a new perspective in psychology and brain engineering since subconscious brain action may be prior to conscious recognition of the intention.

Keywords: Readiness potential, auditory stimuli, event-related potential, electroencephalography, oddball task.

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Authors: M. Sadeghi, A.H. Jafari, S.M.P. Firoozabadi

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Deep Brain Stimulation or DBS is a surgical treatment for Parkinson-s Disease with three stimulation parameters: frequency, pulse width, and voltage. The parameters should be selected appropriately to achieve effective treatment. This selection now, performs clinically. The aim of this research is to study chaotic behavior of recorded tremor of patients under DBS in order to present a computational method to recognize stimulation optimum voltage. We obtained some chaotic features of tremor signal, and discovered embedding space of it has an attractor, and its largest Lyapunov exponent is positive, which show tremor signal has chaotic behavior, also we found out, in optimal voltage, entropy and embedding space variance of tremor signal have minimum values in comparison with other voltages. These differences can help neurologists recognize optimal voltage numerically, which leads to reduce patients' role and discomfort in optimizing stimulation parameters and to do treatment with high accuracy.

Keywords: Chaos, Deep Brain Stimulation, Parkinson's Disease, Stimulation Parameters, tremor.

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Authors: A. Vani, M. N. Mamatha

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Visual signal processing in human beings occurs in the occipital lobe of the brain. The signals that are generated in the brain are universal for all the human beings and they are called Visual Evoked Potential (VEP). Generally, the visually impaired people lose sight because of severe damage to only the eyes natural photo sensors, but the occipital lobe will still be functioning. In this paper, a technique of artificially generating VEP is proposed to enhance the visual ability of the subject. The system uses the electrical photoreceptors to capture image, process the image, to detect and recognize the subject or object. This voltage is further processed and can transmit wirelessly to a BIOMEMS implanted into occipital lobe of the patient’s brain. The proposed BIOMEMS consists of array of electrodes that generate the neuron potential which is similar to VEP of normal people. Thus, the neurons get the visual data from the BioMEMS which helps in generating partial vision or sight for the visually challenged patient. 

Keywords: Visual evoked potential, OpenViBe, BioMEMS, Neuro prosthesis.

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Authors: Ashwani K. Rana, Narottam Chand, Vinod Kapoor

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Aggressive scaling of MOS devices requires use of ultra-thin gate oxides to maintain a reasonable short channel effect and to take the advantage of higher density, high speed, lower cost etc. Such thin oxides give rise to high electric fields, resulting in considerable gate tunneling current through gate oxide in nano regime. Consequently, accurate analysis of gate tunneling current is very important especially in context of low power application. In this paper, a simple and efficient analytical model has been developed for channel and source/drain overlap region gate tunneling current through ultra thin gate oxide n-channel MOSFET with inevitable deep submicron effect (DSME).The results obtained have been verified with simulated and reported experimental results for the purpose of validation. It is shown that the calculated tunnel current is well fitted to the measured one over the entire oxide thickness range. The proposed model is suitable enough to be used in circuit simulator due to its simplicity. It is observed that neglecting deep sub-micron effect may lead to large error in the calculated gate tunneling current. It is found that temperature has almost negligible effect on gate tunneling current. It is also reported that gate tunneling current reduces with the increase of gate oxide thickness. The impact of source/drain overlap length is also assessed on gate tunneling current.

Keywords: Gate tunneling current, analytical model, gate dielectrics, non uniform poly gate doping, MOSFET, fringing field effect and image charges.

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Authors: Ravi Kiran Pothamsetty, Radha Rani Ghosh, Baby Paul Thaliath

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Radiation therapy has undergone many advancements and evloved from 2D to 3D. Recently, with rapid pace of drug discoveries, cutting edge technology, and clinical trials has made innovative advancements in computer technology and treatment planning and upgraded to intensity modulated radiotherapy (IMRT) which delivers in homogenous dose to tumor and normal tissues. The present study was a hospital-based experience comparing two different conformal radiotherapy techniques for brain tumors. This analytical study design has been conducted at Regional Cancer Centre, India from January 2014 to January 2015. Ten patients have been selected after inclusion and exclusion criteria. All the patients were treated on Artiste Siemens Linac Accelerator. The tolerance level for maximum dose was 6.0 Gyfor lenses and 54.0 Gy for brain stem, optic chiasm and optical nerves as per RTOG criteria. Mean and standard deviation values of PTV98%, PTV 95% and PTV 2% in IMRT were 93.16±2.9, 95.01±3.4 and 103.1±1.1 respectively; for 3DCRT were 91.4±4.7, 94.17±2.6 and 102.7±0.39 respectively. PTV max dose (%) in IMRT and 3D-CRT were 104.7±0.96 and 103.9±1.0 respectively. Maximum dose to the tumor can be delivered with IMRT with acceptable toxicity limits. Variables such as expertise, location of tumor, patient condition, and TPS influence the outcome of the treatment.

Keywords: IMRT, 3D CRT, Brain, tumors, OARs, RTOG.

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Authors: Abraham Varghese, Kannan Balakrishnan, Reji R. Varghese, Joseph S. Paul

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Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.

Keywords: Local Binary pattern (LBP), Modified Local Binary pattern (MOD-LBP), T1 and T2 weighted images, Moment features.

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Authors: Nebi Gedik

Abstract:

Electroencephalography (EEG) investigations of the brain computer interfaces are based on the electrical signals resulting from neural activities in the brain. In this paper, it is offered a method for classifying motor imagery EEG signals. The suggested method classifies EEG signals into two classes using the wave atom transform, and the transform coefficients are assessed, creating the feature set. Classification is done with SVM and k-NN algorithms with and without feature selection. For feature selection t-test approaches are utilized. A test of the approach is performed on the BCI competition III dataset IIIa.

Keywords: motor imagery, EEG, wave atom transform, SVM, k-NN, t-test

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Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: Brain activity, EEG, information processing model, model human processor.

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Authors: Suprijanto, Linda Sari, Vebi Nadhira , IGN. Merthayasa. Farida I.M

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Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.

Keywords: Multimodal Emotion Detection, EEG, Facial Image, Optical Flow, compass mapping, Brain Wave

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Authors: Lakshmi Dhandabani, Rajeev Sukumaran

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Aim of this research study is to investigate and establish the characteristics of brain dominances (BD) and multiple intelligences (MI). This experimentation has been conducted for the sample size of 552 undergraduate computer-engineering students. In addition, mathematical formulation has been established to exhibit the relation between thinking and intelligence, and its correlation has been analyzed. Correlation analysis has been statistically measured using Pearson’s coefficient. Analysis of the results proves that there is a strong relational existence between thinking and intelligence. This research is carried to improve the didactic methods in engineering learning and also to improve e-learning strategies.

Keywords: Thinking style assessment, correlational analysis, mathematical model, data analysis, dynamic equilibrium.

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Authors: K. Amudha, C. Nelson Kennedy Babu, S. Balu

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The security of the medical images and its related data is the major research area which is to be concentrated in today’s era. Security in the medical image indicates that the physician may hide patients’ related data in the medical image and transfer it safely to a defined location using reversible watermarking. Many reversible watermarking methods had proposed over the decade. This paper enhances the security level in brain tumor images to hide the patient’s detail, which has to be conferred with other physician’s suggestions. The details or the information will be hidden in Non-ROI area of the image by using the block cipher algorithm. The block cipher uses different keys to extract the details that are difficult for the intruder to detect all the keys and to spot the details, which are the key advantage of this method. The ROI is the tumor area and Non-ROI is the area rest of ROI. The Non-ROI should not be spoiled in any cause and the details in the Non-ROI should be extracted correctly. The reversible watermarking method proposed in this paper performs well when compared to existing methods in the process of extraction of an original image and providing information security.

Keywords: Brain tumor images, Block Cipher, Reversible watermarking, ROI.

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of acquiring new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used in this work is to analyze the dynamics of different brain areas during a cognitive activity to find the relationships between the other areas analyzed to understand the functioning of neural networks better. Also, the latest advances in neuroscience have revealed the exis-tence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neurodevelopmental difficulties for their subsequent assessment and therapy. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process, specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho-pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, learning disabilities, neural networks

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

Abstract:

The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: Functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity.

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Authors: Bayar Shahab

Abstract:

The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements. 

Keywords: BCI, CCA, SSVEP, EEG

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Authors: FH. Sarieddeen, R. El Berbari, S. Imad, J. Abdel Baki, M. Hamad, R. Blanc, A. Nakib, Y.Chenoune

Abstract:

Brain ArterioVenous Malformation (BAVM) is an abnormal tangle of brain blood vessels where arteries shunt directly into veins with no intervening capillary bed which causes high pressure and hemorrhage risk. The success of treatment by embolization in interventional neuroradiology is highly dependent on the accuracy of the vessels visualization. In this paper the performance of clustering techniques on vessel segmentation from 3- D rotational angiography (3DRA) images is investigated and a new technique of segmentation is proposed. This method consists in: preprocessing step of image enhancement, then K-Means (KM), Fuzzy C-Means (FCM) and Expectation Maximization (EM) clustering are used to separate vessel pixels from background and artery pixels from vein pixels when possible. A post processing step of removing false-alarm components is applied before constructing a three-dimensional volume of the vessels. The proposed method was tested on six datasets along with a medical assessment of an expert. Obtained results showed encouraging segmentations.

Keywords: Brain arteriovenous malformation (BAVM), 3-D rotational angiography (3DRA), K-Means (KM) clustering, Fuzzy CMeans (FCM) clustering, Expectation Maximization (EM) clustering, volume rendering.

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Authors: Nebi Gedik

Abstract:

The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.

Keywords: motor imagery, EEG, Wave atom transform sub-bands, SVM, k-NN

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