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In this contribution, we investigate the effect of a resonant high-impedance shield as a part of a birdcage coil for human brain imaging at 7 T. On a homogeneous phantom, it was shown that the loading of the birdcage coil to the sample can be enhanced by replacing a conventional copper shield with the high-impedance boundary with frequency-dependent surface impedance. To demonstrate this effect, we perform a numerical simulation of an extended birdcage coil with the high-impedance shield. The extended coil is much more comfortable for a patient and creates a lower local SAR. It has been shown that the extended coil equipped with the high-impedance shield has the same efficiency as a standard-size coil with the copper shield. [ABSTRACT FROM AUTHOR]

This study aims to sense trust and distrust in a real-time inspired scenario through the classification of brain signals. Here, a word elicitation study is used to invoke the mental state associated with trust and distrust associated with the machine. Participants think of any event or experience that comes into their mind when they observe the word. They think or recall that event/experience without deliberately filtering out any kind of cognitive or affective mental state, which we consider as a replica of a real-life scenario where all kinds of mental states or emotions possibly co-exist along with trust or distrust. While thinking or recalling such events, Electroencephalography data is recorded from the participants' cortex and analyzed through Machine Learning approaches with several classification algorithms. The study developed an approach to sense whether the human is going through trust or distrust and compared different methods to discuss their efficacies in different scenarios. Here, the individualistic and generalistic approach is delved into, and it found that individualistic approaches provide better accuracy in sensing trust or distrust state of the human brain. Also, this study explored ways to increase the efficiency of the method by reducing the number of channels and compared the performance of the models by observing the loss of accuracy caused by the reduced number of channels. This study found that the K-Nearest Neighbor and/or Random Forest classifier algorithm provides the best result using raw data with the individualistic approach in most scenarios, achieving up to 100% average accuracy. [ABSTRACT FROM AUTHOR]

In the recent past, Computer Aided Diagnosis (CAD) technologies plays vital role in computer vision in the field of medical diagnosis. Few nervous disorders and describing compulsive area are analyzed, and structural human organ brain is undergone for research with the support of magnetic resonance imaging (MRI) imaging. Standard classification machine learning algorithms are being used in analysis for the automated diagnosis of Alzheimer's disease (AD), and machine learning-based methods have become a common alternative for AD diagnosis. State-of-the-art approaches that take diagnosis into account have been shown to be more accurate than conventional evaluation. Obtaining information from various approaches, on the other hand, is time-consuming and costly, and certain methodologies could have energy adverse effects. Our research is based on computerized automated Magnetic Resonance Imaging (MRI). In this research, automatic object segmentation and classification of Alzheimer disease (AD) is proposed. The collected MRI brain images contain various noises such as salt and pepper noise, speckle noise and Gaussian noise. The 2D Adaptive Median Filter (AMF) is proposed to filter all types of noises. The filtered image is further enhanced to improve the quality of image. The significant Edge Preservation-Contrast Limited Adaptive Histogram Equalization (EP-CLAHE) algorithm is used to improve the quality of image in terms of contrast and brightness. The AD region from MRI brain image is further clustered using Efficient Fuzzy based C Means Clustering (EFCMC). The clustered AD region is further segmented using Adaptive Otsu Thresholding (AOT). The significant features are calculated using Gray Smooth Co-occurrence Vector (GSCV). The 1 Dimensional Iterative Convolutional Neural Network (1D I) is used to classify the AD stages. The experimental results show that the proposed methodology performance is better than conventional methodologies. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) is applied to study brain connectivity. The conventional methods such as independent component analysis (ICA) address the problem by utilizing only the marginal information. An improvement has been suggested by exploiting the temporal or spatial correlation structure and has been demonstrated to be very important for extracting brain networks. A challenge for ICA has been the problem of assessing the significance of the independent components (IC) or latent sources. We propose to examine this problem by using a bootstrap method to resample the data. The procedure allows one to set confidence intervals for parameters or features related to the latent sources. An application based on EEG data will be illustrated to test if the source is truly a signal or noise. [ABSTRACT FROM AUTHOR]

Throughout the last ten years, neuromarketing has been developing as a completely new branch of marketing. Considering the characteristics of tourism as a complex social construct, an assumption stands that a neuromarketing research can have a significant potential for optimizing the destination offer to match the desired perception of potential guests. The appliance of neuromarketing tourism market research thus assumes a novel conceptual approach, one that quite differs from the standard. The secondary data on the application of numerous neuromarketing techniques was gathered from articles, monographs and other relevant sources. Also, descriptive, comparative and compiling research methods were used. Primary research consisted of an in-depth interview with a group of tourism board representatives and a nonrandom, purposive sample of expert tourism workers, who filled a survey on their attitudes toward suitability and applicability of the neuromarketing research techniques. Neuromarketing techniques should be used, by only in tandem with the standard research methodology, primarily conducted by marketing staff, with physicians assisting in MR1 imaging interpretation and technology support. These projects should be conducted on a national level, for the research is capital intensive and its results have a wide area of repercussions. When consulting available databases, no research has been found dealing with the application of neuromarketing in destination's brand management. The contribution thus consists of the comparison carried out, between neuromarketing techniques and the neuromarketing potential, and the standard research methods, valued and contrasted to the emerging discipline of neuromarketing. [ABSTRACT FROM AUTHOR]