Search results for: brain computer interface (BCI)

Authors: J. M. Lee, W. R. Noh, C. Y. Kim, M. G. Lee

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Delamination of oxide scale has been often discovered at the interface between Fe carbon steel and oxide scale. Among several mechanisms of this delamination behavior, the normal tensile stress to the substrate-scale interface has been described as one of the main factors. The stress distribution at the interface is also known to be affected by thermal expansion mismatch between substrate and oxide scale, creep behavior during cooling and the geometry of the interface. In this study, stress states near the interface in a Fe carbon steel with oxide scale have been investigated using FE simulations. The thermal and mechanical properties of oxide scales are indicated in literature and Fe carbon steel is measured using tensile testing machine. In particular, the normal and shear stress components developed at the interface during bending are investigated. Preliminary numerical sensitivity analyses are provided to explain the effects of the interface geometry and oxide thickness on the delamination behavior.

Keywords: oxide scale, delamination, Fe analysis, roughness, thickness, stress state

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Rica Jell de Laza, Jose Alberto Fernandez, Andrea Marie Mendoza, Qristin Jeuel Regalado

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This study investigates whether participants experience different levels of persuasion depending on the hemisphere of the brain and the tone of voice. The experiment was performed on 96 volunteer undergraduate students taking an introductory course in psychology. The participants took part in a 2 x 3 (Hemisphere: left, right x Tone of Voice: positive, neutral, negative) Mixed Factorial Design to measure how much a person was persuaded. Results showed that the hemisphere of the brain and the tone of voice used did not significantly affect the results individually. Furthermore, there was no interaction effect. Therefore, the hemispheres of the brain and the tone of voice employed play insignificant roles in persuading a person.

Keywords: dichotic listening, brain hemisphere, tone of voice, persuasion

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Authors: Thomas Ousterhout

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In an effort to test the ability of a commercial grade EEG headset to effectively measure the N400 ERP, a replication study was conducted to see if similar results could be produced as that which used a medical grade EEG. Pictures of meaningful and meaningless hand postures were borrowed from the original author and subjects were required to perform a semantic discrimination task. The N400 was detected indicating semantic processing of the meaningfulness of the hand postures. The results corroborate those of the original author and support the use of some commercial grade EEG headsets for non-critical research applications.

Keywords: EEG, ERP, N400, semantics, congruency, gestures, emotiv

Procedia PDF Downloads 241

Authors: Mahdi Farajzadeh Ajirlou

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Ordinary fetal brain development can be considered by in vivo attractive reverberation imaging (MRI) from the 18th gestational week (GW) to term and depends fundamentally on T2-weighted and diffusion-weighted (DW) arrangements. The foremost commonly suspected brain pathologies alluded to fetal MRI for assist assessment are ventriculomegaly, lost corpus callosum, and anomalies of the posterior fossa. Brain division could be a crucial to begin with step in neuroimage examination. Within the case of fetal MRI it is especially challenging and critical due to the subjective introduction of the hatchling, organs that encompass the fetal head, and irregular fetal movement. A few promising strategies have been proposed but are constrained in their execution in challenging cases and in realtime division. Fetal MRI is routinely performed on a 1.5-Tesla scanner without maternal or fetal sedation. The mother lies recumbent amid the course of the examination, the length of which is ordinarily 45 to 60 minutes. The accessibility and continuous approval of standardizing fetal brain development directions will give critical devices for early discovery of impeded fetal brain development upon which to oversee high-risk pregnancies.

Keywords: brain, fetal, MRI, imaging

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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The widespread adoption and increasing proliferation of smartphones that started during the first decade of the twenty-first century have enabled their users to communicate and access information in ways that were merely thought of as possibilities in the few years before the smartphone revolution. A product of the convergence of the cellular phone and portable computer, the smartphone provides an additional important function that used to be the exclusive domain of desktop-bound computers and portable computers: Web Browsing. For increasing numbers of users, the smartphone and allied devices such as tablet computers have become their first and often their only means of accessing the World Wide Web. This has led to the development of websites that cater to the needs of the new breed of smartphone-carrying web users. The smaller size of smartphones as compared with conventional computers has provided unique challenges to web interface designers. The smaller screen size and touchscreen interface have made it much more difficult to read and navigate through web pages that were in most part designed for traditional desktop and portable computers. Although increasing numbers of websites now provide an alternate website formatted for smartphones, problems with ease of use, reliability and usability still remain. This study focuses on the identification of the problems associated with smartphone web interfaces, the compliance with accepted standards of user-oriented web interface design, the strategies that could be utilized to ensure the design of user-centric web interfaces for smartphones, and the identification of the current trends and developments related to user-centric web interface design intended for the consumption of smartphone users.

Keywords: human-computer interaction, user-centered design, web interface, mobile, smartphone

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding

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Authors: A. Albar, U. Schwingenschlogel

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Device applications of transparent conducting oxides require a thorough understanding of the physical and chemical properties of the involved interfaces. We use ab-initio calculations within density functional theory to investigate the electronic states at the SnO/SnO2 hetero-interface. Tin dioxide and monoxide are transparent materials with high n-type and p-type mobilities, respectively. This work aims at exploring the modifications of the electronic states, in particular the charge transfer, in the vicinity of the hetero-interface. The (110) interface is modeled by a super-cell approach in order to minimize the mismatch between the lattice parameters of the two compounds. We discuss the electronic density of states as a function of the distance to the interface.

Keywords: density of states, ab-initio calculations, interface states, charge transfer

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: J. R. Ashrapov, G. A. Alihodzhaeva, D. E. Abdullaev, N. R. Kadirbekov

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Diagnosis of brain tumors is one of the challenges, as several central nervous system diseases run the same symptoms. Modern diagnostic techniques such as CT, MRI helps to significantly improve the surgery in the operating period, after surgery, after allowing time to identify postoperative complications in neurosurgery. Purpose: To study the MRI characteristics and localization of brain tumors in children and to detect the postoperative complications in the postoperative period. Materials and methods: A retrospective study of treatment of 62 children with brain tumors in age from 2 to 5 years was performed. Results of the review: MRI scan of the brain of the 62 patients 52 (83.8%) case revealed a brain tumor. Distribution on MRI of brain tumors found in 15 (24.1%) - glioblastomas, 21 (33.8%) - astrocytomas, 7 (11.2%) - medulloblastomas, 9 (14.5%) - a tumor origin (craniopharyngiomas, chordoma of the skull base). MRI revealed the following characteristic features: an additional sign of the heterogeneous MRI signal of hyper and hypointensive T1 and T2 modes with a different perifocal swelling degree with involvement in the process of brain vessels. The main objectives of postoperative MRI study are the identification of early or late postoperative complications, evaluation of radical surgery, the identification of the extended-growing tumor that (in terms of 3-4 weeks). MRI performed in the following cases: 1. Suspicion of a hematoma (3 days or more) 2. Suspicion continued tumor growth (in terms of 3-4 weeks). Conclusions: Magnetic resonance tomography is a highly informative method of diagnostics of brain tumors in children. MRI also helps to determine the effectiveness and tactics of treatment and the follow up in the postoperative period.

Keywords: brain tumors, children, MRI, treatment

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Authors: Mohammad Hashemi, John Herbert

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Quantifying and measuring Quality of Experience (QoE) are important and difficult concerns in Human Computer Interaction (HCI). Quality of Service (QoS) and the actual User Interface (UI) of the application are both important contributors to the QoE of a user. This paper describes a framework that measures accurately the way a user uses the UI in order to model users' behaviours and profiles. It monitors the use of the mouse and use of UI elements with accurate time measurement. It does this in real-time and does so unobtrusively and efficiently allowing the user to work as normal with the application. This real-time accurate measurement of the user's interaction provides valuable data and insight into the use of the UI, and is also the basis for analysis of the user's QoE.

Keywords: user modelling, user interface experience, quality of experience, user experience, human and computer interaction

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Authors: Hana Almakky, Reza Sahandi, Jacqui Taylor

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Social media continue to grow, and user interfaces may become more appealing if cultural characteristics are incorporated into their design. Facebook was designed in the west, and the original language was English. Subsequently, the words in the user interface were translated to other languages, including Arabic. Arabic words are written from right to left, and English is written from left to right. The translated version may misrepresent the original design and users preferences may influence their culture, which should be considered in the user interface design. Previous research indicates that users are more comfortable when interacting with a user interface, which relates to their own culture. Therefore, this paper, using a survey investigates the preferences of Saudi Arabian on the Arabic version of user interface of Facebook.

Keywords: culture, social media, user interface design, Facebook, Saudi Arabia

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Authors: Gil-Eon Jeong, Sung-Kie Youn, K. C. Park

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Treatment for the non-matching interface is an important computational issue. To handle this problem, the method of Lagrange multipliers including classical and localized versions are the most popular technique. It essentially imposes the interface compatibility conditions by introducing Lagrange multipliers. However, the numerical system becomes unstable and inefficient due to the Lagrange multipliers. The interface element-independent formulation that does not include the Lagrange multipliers can be obtained by modifying the independent variables mathematically. Through this modification, more efficient and stable system can be achieved while involving equivalent accuracy comparing with the conventional method. A numerical example is conducted to verify the validity of the presented method.

Keywords: element-independent formulation, interface coupling, methods of Lagrange multipliers, non-matching interface

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Authors: Vince Macri, Jakub Petioky, Paul Zilber

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Virtual-reality-based technology, i.e. video-game-like simulations (collectively, VRSims) are used in therapy for a variety of medical conditions. The purpose of this paper is to contribute to a discussion on criteria for selecting VRSims to augment treatment of survivors of acquired brain injury. Specifically, for treatments to improve or restore brain motor function in upper extremities affected by paresis or paralysis. Six uses of virtual reality are reviewed video games for entertainment, training simulations, unassisted or device-assisted movements of affected or unaffected extremities displayed in virtual environments and virtual anatomical interactivity.

Keywords: acquired brain injury, brain-motor function, virtual anatomical interactivity, therapeutic simulations

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Authors: Moon Jung Kim, Guil Rhim

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The clinical sensitivity of the “VEUPLEXTM TBI assay”, a clinical trial medical device, in mild traumatic brain injury was 28.6% (95% CI, 19.7%-37.5%), and the clinical specificity was 94.0% (95% CI, 89.3%). -98.7%). In addition, when the results analyzed by marker were put together, the sensitivity was higher when interpreting the two tests together than the two tests, UCHL1 and GFAP alone. Additionally, when sensitivity and specificity were analyzed based on CT results for the mild traumatic brain injury patient group, the clinical sensitivity for 2 CT-positive cases was 50.0% (95% CI: 1.3%-98.7%), and 19 CT-negative cases. The clinical specificity for cases was 68.4% (95% CI: 43.5% - 87.4%). Since the low clinical sensitivity for the two CT-positive cases was not statistically significant due to the small number of samples analyzed, it was judged necessary to secure and analyze more samples in the future. Regarding the clinical specificity analysis results for 19 CT-negative cases, there were a large number of patients who were actually clinically diagnosed with mild traumatic brain injury but actually received a CT-negative result, and about 31.6% of them showed abnormal results on VEUPLEXTM TBI assay. Although traumatic brain injury could not be detected in 31.6% of the CT scans, the possibility of actually suffering a mild brain injury could not be ruled out, so it was judged that this could be confirmed through follow-up observation of the patient. In addition, among patients with mild traumatic brain injury, CT examinations were not performed in many cases because the symptoms were very mild, but among these patients, about 25% or more showed abnormal results in the VEUPLEXTM TBI assay. In fact, no damage is observed with the naked eye immediately after traumatic brain injury, and traumatic brain injury is not observed even on CT. But in some cases, brain hemorrhage may occur (delayed cerebral hemorrhage) after a certain period of time, so the patients who did show abnormal results on VEUPLEXTM TBI assay should be followed up for the delayed cerebral hemorrhage. In conclusion, it was judged that it was difficult to judge mild traumatic brain injury with the VEUPLEXTM TBI assay only through clinical findings without CT results, that is, based on the GCS value. Even in the case of CT, it does not detect all mild traumatic brain injury, so it is difficult to necessarily judge that there is no traumatic brain injury, even if there is no evidence of traumatic brain injury in CT. And in the long term, more patients should be included to evaluate the usefulness of the VEUPLEXTM TBI assay in the detection of microscopic traumatic brain injuries without using CT.

Keywords: brain injury, traumatic brain injury, GFAP, UCHL1

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: Yusri Yusof, Kamran Latif

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Computer Numeric Control (CNC) machine has been widely used in the industries since its inception. Currently, in CNC technology has been used for various operations like milling, drilling, packing and welding etc. with the rapid growth in the manufacturing world the demand of flexibility in the CNC machines has rapidly increased. Previously, the commercial CNC failed to provide flexibility because its structure was of closed nature that does not provide access to the inner features of CNC. Also CNC’s operating ISO data interface model was found to be limited. Therefore, to overcome that problem, Open Architecture Control (OAC) technology and STEP-NC data interface model are introduced. At present the Personal Computer (PC) has been the best platform for the development of open-CNC systems. In this paper, both ISO data interface model interpretation, its verification and execution has been highlighted with the introduction of the new techniques. The proposed is composed of ISO data interpretation, 3D simulation and machine motion control modules. The system is tested on an old 3 axis CNC milling machine. The results are found to be satisfactory in performance. This implementation has successfully enabled sustainable manufacturing environment.

Keywords: CNC, ISO 6983, ISO 14649, LabVIEW, open architecture control, reconfigurable manufacturing systems, sustainable manufacturing, Soft-CNC

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Authors: Hassane Ben Slimane, Dennai Benmoussa, Abderrachid Helmaoui

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We have theoretically calculated the photovoltaic conversion efficiency of a graded interface CdS/CIGS solar cell, which can be experimentally fabricated. Because the conduction band discontinuity or spike in an abrupt heterojunction CdS/CIGS solar cell can hinder the separation of hole-electron by electric field, a graded interface layer is uses to eliminate the spike and reduces recombination in space charge region. This paper describes the role of the graded band gap interface layer in decreasing the performance of the heterojunction cell. By optimizing the thickness of the graded region, an improvement of conversion efficiency has been observed in comparison to the conventional CIGS system.

Keywords: heterojunction, solar cell, graded interface, CIGS

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Authors: Alexander N. Pisarchik, Parth Chholak

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Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise.

Keywords: brain, flickering, magnetoencephalography, MEG, visual perception, perception time

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Authors: Noriyoshi Yamauchi, Etsuo Horikawa, Takunori Tsuji

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Training by exoskeleton robot is drawing attention as a rehabilitation method for body paralysis seen in many cases, and there are many forms that assist with the myoelectric signal generated by exercise commands from the brain. Rehabilitation requires more frequent training, but it is one of the reasons that the technology is required for the identification of the myoelectric potential derivation site and attachment of the device is preventing the spread of paralysis. In this research, we focus on improving the efficiency of gait training by exoskeleton type robots, improvement of myoelectric acquisition and analysis method using active matrix sensing method, and improvement of walking rehabilitation and walking by optimization of robot control.

Keywords: active matrix sensing, brain machine interface (BMI), the central pattern generator (CPG), myoelectric signal processing, robot rehabilitation

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Authors: Elisaveta Kirilova, Wilfried Becker, Jordanka Ivanova, Tatyana Petrova

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The study is a continuation of the research on the hygrothermal piezoelectric response of a smart patch/layer joint with undesirable interface defect (gap) at dynamic time harmonic mechanical and electrical load and environmental conditions. In order to find the axial displacements, shear stress and interface debond length in a closed analytical form for different positions of the interface gap, the 1D modified shear lag analysis is used. The debond length is represented as a function of many parameters (frequency, magnitude, electric displacement, moisture and temperature, joint geometry, position of the gap along the interface, etc.). Then the Genetic algorithm (GA) is implemented to find this position of the gap along the interface at which a vanishing/minimal debond length is ensured, e.g to find the most harmless position for the safe work of the structure. The illustrative example clearly shows that analytical shear-lag solutions and GA method can be combined successfully to give an effective prognosis of interface shear stress and interface delamination in patch/layer structure at combined loading with existing defects. To show the effect of the position of the interface gap, all obtained results are given in figures and discussed.

Keywords: genetic algorithm, minimal delamination, optimal gap position, shear lag solution

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Authors: Katalin Bándy

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The European emigration of highly educated immigrants draws attention to the problem of brain drain. Due to the Eastern European countries joining the EU and the opening of the Western European labour market the west-wards migration brisked up. By now another problem has been intensified correlated to migration: the migration of highly skilled workers related to brain waste tendencies. With some exceptions, educated immigrants from Eastern European countries are more likely to end up in unskilled jobs than residents. This paper is about to reveal the above-mentioned problems and this study is supported by the results of secondary pieces of research and the own survey made in the EU-15 among the Hungarian highly skilled (especially economics graduated) migrants, and it also examines the causes and in the focus there are the migrant motivations of the high-skilled young generation after the crisis.

Keywords: brain drain, brain waste, migration of highly-skilled, underemployment

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Authors: K. Doi, T. Nishimura, M. Umeda

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There are very few sound interfaces that both healthy people and hearing handicapped people can use to play together. In this study, we developed a sound tactile interface that makes use of the human sensation of stiffness. The interface comprises eight elastic objects having varying degrees of stiffness. Each elastic object is shaped like a column. When people with and without hearing disabilities press each elastic object, different sounds are produced depending on the stiffness of the elastic object. The types of sounds used were “Do Re Mi sounds.” The interface has a major advantage in that people with or without hearing disabilities can play with it. We found that users were able to recognize the hardness sensation and relate it to the corresponding Do Re Mi sounds.

Keywords: tactile sense, sound interface, stiffness perception, elastic object

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: J. Branstett, V. Gagneux, A. Leleu, B. Levadoux, J. Pascale

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This paper presents the interface CONDUCTHOME which controls home automation systems with a Leap Motion using ‘invariant gesture protocols’. The function of this interface is to simplify the interaction of the user with its environment. A hardware part allows the Leap Motion to be carried around the house. A software part interacts with the home automation box and displays the useful information for the user. An objective of this work is the development a natural/invariant/simple gesture control interface to help elder people/people with disabilities.

Keywords: automation, ergonomics, gesture recognition, interoperability

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Authors: Jozsef Katona, Attila Kovari, Tibor Ujbanyi, Gergely Sziladi

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By the development of IT systems, human-computer interaction is also developing even faster and newer communication methods become available in human-machine interaction. In this article, the application of a hand gesture controlled human-computer interface is being introduced through the example of a mobile robot. The control of the mobile robot is implemented in a realistic virtual environment that is advantageous regarding the aspect of different tests, parallel examinations, so the purchase of expensive equipment is unnecessary. The usability of the implemented hand gesture control has been evaluated by test subjects. According to the opinion of the testing subjects, the system can be well used, and its application would be recommended on other application fields too.

Keywords: human-machine interface (HCI), mobile robot, hand control, virtual environment

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Authors: Berkan Ural, Arif Eser, Sinan Apaydin

Abstract:

Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

Procedia PDF Downloads 384

Authors: Duy Cuong Nguyen, Ali Makke, Guillaume Montay

Abstract:

This work presents an improved single fiber pull-out test for fiber/matrix interface characterization. This test has been used to study the Inter-Facial Shear Strength ‘IFSS’ of hemp fibers reinforced polypropylene (PP). For this aim, the fiber diameter has been carefully measured using a tomography inspired method. The fiber section contour can then be approximated by a circle or a polygon. The results show that the IFSS is overestimated if the circular approximation is used. The Influence of the molding temperature on the IFSS has also been studied. We find a molding temperature of 183°C leads to better interface properties. Above or below this temperature the interface strength is reduced.

Keywords: composite, hemp, interface, pull-out, processing, polypropylene, temperature

Procedia PDF Downloads 363