Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy

Altered electroencephalography (EEG) activity in schizotypal individuals is a powerful indicator of proneness towards psychosis. This alteration is beyond decreased alpha power often measured in resting state EEG. Multiscale fluctuation dispersion entropy (MFDE) measures the non-linear complexity of the fluctuations of EEGs and is a more effective approach compared to the traditional linear power spectral density (PSD) measures of EEG activity in patients with neurodegenerative disorders. In this study, we applied MFDE to EEG signals to distinguish high schizotypy (HS) and low schizotypy (LS) individuals. The study includes several trials from 29 participants psychometrically classified as HS (n=19) and LS (n=10). After preprocessing, MFDE was computed in frontal, parietal, central, temporal and occipital regions for each participant at multiple time scales. Statistical analysis and machine learning algorithms were used to calculate the differences in MFDE measures between the HS and LS groups. Our findings revealed significant differences in MFDE measures between LS and HS individuals in the delta frequency band (at time scale 100 ms). HS individuals exhibited increased complexity and irregularity compared to LS individuals in the delta frequency band particularly in the occipital region. Furthermore, the MFDE measures resulted in high accuracy (96.55%) in discriminating between HS and LS individuals and outperformed the models based on power spectrum, demonstrating the potential of MFDE as a neurophysiological marker for schizotypy traits. The increased non-linear fluctuation in delta frequency band in the occipital region of HS individuals implies the changes in cognitive functions, such as memory and attention, and has significant potential as a biomarker for schizotypy and proneness towards psychosis

Dependence of Carbon Nanotube Field Effect Transistors Performance on Doping Level of Channel at Different Diameters: On/off current ratio

Choosing a suitable doping level of channel relevant to channel diameter is considered for determining the carbon nanotube field effect transistors' performance which seem to be the best substitute of current transistor technology. For low diameter values of channel the ratio of on/off current declines by increasing the doping level. But for higher diameter values there is an optimum point of doping level in obtaining the highest on/off current ratio. For further verification, the variations of performance are justified by electron distribution function's changes on energy band diagram of these devices. The results are compared at two different gate fields.Comment: 9 double spaced pages, 4 figures, published in applied physics letters, along with the terms of the American Institute of Physics Transfer of Copyright Agreement at first pag

A Nonlinear Method to Estimate Simultaneous Force Pattern Generated by Hand Fingers; Application in Prosthetic Hand

In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of sEMG signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R2 criterion. The Total value of R2 for 6 DoF was 92.8±5.2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous myoelectric control

Ultra Low Power Symmetric Pass Gate Adiabatic Logic with CNTFET for Secure IoT Applications

With the advent and development of the Internet of Things, new needs arose and more attention was paid to these needs. These needs include: low power consumption, low area consumption, low supply voltage, higher security and so on. Many solutions have been proposed to improve each one of these needs. In this paper, we try to reduce the power consumption and enhance the security by using SPGAL, a DPA-resistant Logic, and Carbon Nanotube FETs (CNTFETs) instead of conventional CMOS and MOSFET technology, for IoT devices. All simulations are done with HSPICE

Behavioral Modeling and Simulation of Semiconductor Devices and Circuits Using VHDL-AMS

During the past few years, a lot of work has been done on behavioral models and simulation tools. But a need for modeling strategy still remains. The VHDL-AMS language supports the description of analog electronic circuits using Ordinary Differential Algebraic Equations (ODAEs), in addition to its support for describing discrete-event systems. For VHDL-AMS to be useful to the analog design community, efficient semiconductor device models must be available. In this paper, potential merits of the new IEEE VHDL-AMS standard in the field of modeling semiconductor devices are discussed. The device models for diodes and the principles, techniques, and methodology used to achieve the design of an analytical third generation Spice transistor MOS model named EKV are presented. This is done by taking into account the thermoelectrical effect in VHDL-AMS, and with relevant parameters set to match a deep submicron technology developed in VHDL-AMS. The models were validated using System Vision from Mentor Graphics

Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy and 11 low schizotypy participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVM) revealed significant differences between high and low schizotypy. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting the schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to early identification of schizophrenia and other spectrum disorders