Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Tonic Pain Experience based on Phase Connectivity in the Alpha Frequency Band of the Electroencephalogram using Convolutional Neural Networks

The complexity of brain activity involved in the generation of the experience of pain makes it hard to identify neural markers able to predict pain states. The within and between subjects variability of pain hinders the predictive potential of machine learning models trained across participants. This challenge can be tackled by implementing deep learning classifiers based on convolutional neural networks (CNNs). We targeted phase-based connectivity in the alpha band recorded with electroencephalography (EEG) during resting states and sensory conditions (eyes open [O] and closed [C] as resting states, and warm [W] and hot [H] water as sensory conditions). Connectivity features were extracted and re-organized as square matrices, because CNNs are effective in detecting the patterns from 2D data. To assess the classifier performance we implemented two complementary approaches: we 1) trained and tested the classifier with data from all participants, and 2) using a leave-one-out approach, that is excluding one participant at a time during training while using their data as a test set. The accuracy of binary classification between pain condition (H) and eyes open resting state (O) was 94.16% with the first approach, and 61.01 % with the leave-one-out approach. Clinical relevance—Further validation of the CNN classifier may help caregivers track the rehabilitation of chronic pain patients and dynamically modify the therapy. Further refinement of the model may allow its application in critical care setting with unresponsive patients to identify pain-like states otherwise incommunicable to medical personnel.
Selective neuromodulation of retinal ganglion cells via a hybrid optic-nerve and retinal neuroprosthesis for visual restoration

A visual neuroprosthesis delivers electrical stimulation to the surviving neural cells of the visual pathway to produce prosthetic vision. While the retina is often chosen as the stimulation site, current retinal prostheses are hindered by the lack of functional selectivity that impairs the resolution. A possible strategy to improve the resolution is to combine the retinal stimulation and the stimulation of the optic nerve bundle, which contains myelinated fibres of retinal ganglion cells (RGCs) axons that vary in diameter. In this study, we used a computational model of retinal ganglion cells (RGCs) with myelinated axons to predict whether the frequency of electrical stimulation delivered to the optic nerve can be modulated to preferentially inhibit a subset of optic nerve fibres classified by diameter. The model combined a finite element model of bipolar penetrating electrodes delivering sinusoidal stimulation in the range of 25–10000 Hz to the optic nerve, and a double-cable model, to represent an optic nerve fibre. We found that the diameter of the axon fibre and ion kinetic properties of the RGC affect the neuron's frequency response, demonstrating the potential of an optic nerve stimulation to produce selective inhibition based on the axon fibre size. Clinical Relevance–This establishes the importance of considering the size of the nerve cell axons, as well as the functional type of the RGC, in stimulating the optic nerve. This can be exploited to facilitate functionally selective neuromodulation when used in conjunction with retinal stimulation.
Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders

Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.
Artificial Neural Networks for Antenna-based Contactless Liquid Classification

Neural networks are enhancing modern day sensors and results for classification and real time monitoring. The complexity of neural networks are enabling the future for image classification, self-driving cars and redefining how information is processed. In this paper, a 4-port antenna configuration was adopted for contactless sensing and characterization of liquids. A standard gain horn antenna with an output power of 17 dBm was placed 50 cm away from a 500 ml plastic container. The response of the system was monitored for two sample liquids: Water and 95% Ethanol. The measured microwave response was then classified across a 3-3.15 GHz frequency range by a trained neural network with a 94.44% accuracy. The results demonstrated the future ability of mixing microwave technology with neural networks for event safety.
Ageing and Growth in the Small Open Economy

We construct an overlapping generations model for the small open economy which incorporates a realistic description of the mortality process. Agents engage in educational activities at the start of life and thus create human capital to be used later on in life for production purposes. Depending on the strength of the intergenerational externality in the human capital production function, the model gives rise to exogenous or endogenous growth. The effects of demographic shocks and fiscal stimuli on the growth path are derived, both at impact, during transition, and in the long run.
Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces

Kernel temporal differences (KTD) $(\lambda)$ algorithm integrated in Q-learning (Q-KTD) has shown its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs). RLBMI with its unique learning strategy based on trial-error allows continuous learning and adaptation in BMIs. Q-KTD has shown good performance in both open and closed-loop experiments for finding a proper mapping from neural intention to control commands of an external device. However, previous studies have been limited to intracortical BMIs where monkey's firing rates from primary motor cortex were used as inputs to the neural decoder. This study provides the first attempt to investigate Q-KTD algorithm's applicability in EEG-based RLBMIs. Two different publicly available EEG data sets are considered, we refer to them as Data set A and Data set B. EEG motor imagery tasks are integrated in a single step center-out reaching task, and we observe the open-loop RLBMI experiments reach 100% average success rates after sufficient learning experience. Data set A converges after approximately 20 epochs for raw features and Data set $\mathrm{B}$ shows convergence after approximately 40 epochs for both raw and Fourier transform features. Although there still exist challenges to overcome in EEG-based RLBMI using Q-KTD, including increasing the learning speed, and optimization of a continuously growing number of kernel units, the results encourage further investigation of Q-KTD in closed-loop RLBMIs using EEG. Clinical Relevance— This study supports feasibility of noninvasive EEG-based RLBMI implementations and addresses benefits and challenges of RLBMI using EEG.
Does Resting Motor Threshold correlate with severity of Alzheimer's disease?

Conflicting results have emerged from studies examining the potential of resting motor threshold (RMT) as a neurophysiological marker for Alzheimer's disease (AD) diagnosis and progression. In this study, we estimated the strength of the association between RMT measurements and severity of cognitive impairment in a relatively large sample (N=128) of clinical trial participants with mild (Clinical Dementia Rating - CDR=1) to moderate (CDR=2) AD. RMT for each participant was determined by applying single-pulse transcranial magnetic stimulation repeated at varying intensities over left and right sides of the primary motor cortex. RMT is the minimum intensity that evoked a visible contralateral involuntary finger twitch and RMT asymmetry is the absolute difference between the left and right RMT measurements. Cognitive impairment was measured with the Montreal Cognitive Assessment (MoCA) and the Alzheimer Disease Assessment Scale - Cognitive (ADAS-Cog) scores. Although the left and right RMT was lower in CDR 2 than in CDR 1 participants, neither RMT nor RMT asymmetry correlated significantly with cognitive test scores. In conclusion, our study in a large sample size does not support the idea that RMT is a sensitive marker of cognitive decline/severity in AD. Clinical Relevance— This study provides evidence that RMT may not be useful for AD progression monitoring
A Software Design Method for Creation and Utilization of Experiential Knowledge

Until now, knowledge-related efforts in engineering, including software engineering, have aimed to automate human activities. But, unfortunately, the use of knowledge for automation leads to the black-boxing of the system, making it difficult to respond flexibly to changes and shaking the reliability, safety, and trustworthiness of computer processing results. When it is necessary to respond to change and diversity and realize a sustainable society, knowledge must be used for change, diversity, and creation for sustainability, not for automating existing mechanisms. To realize such knowledge creation and utilization, it is necessary to design a software support environment and make it available as a platform for knowledge creation. This paper will show the design and effect of a knowledge creation platform for human beings to use knowledge in their activities and evolve, deepen, continue, and maintain their activities.
Power Management of Islanded Solar PV-Battery System for Submersible Pump Induction Motor and Local Loads

This work presents an effective control and power management of the system comprising of solar PV and battery assisted for submersible pump induction motor and single-phase inverter fed local loads for residential application. An intelligent power management scheme is developed for solar PV/battery system with robust and fast control of DC link voltage under all dynamic conditions. The perturb and observe MPPT algorithm utilized here charges the battery energy storage (BES) as per the PV power availability taking the battery SOC (state of charge) boundary conditions into consideration. The control strategy works for load follower mode (LFM) as per load demand and charging status of the battery. The bidirectional converter of battery reduces the battery size while regulating the DC link voltage. The presented system is validated through MATLAB/Simulink. This topology and its control are validated through test results.
Optimal voltage control in interconnected power distribution systems by a self-adaptive fuzzy logic controller

The high penetration of distributed generation (DG) is changing the energy flows in the grid, as well as the energy losses and voltage profile of the system. To solve these problems, the impact of the connection of Renewable Energy Generators (REGs) on the grid is being studied. This work is intended to present the voltage variation of each node of the distribution grid before and after the insertion of a wind turbine or solar panel and the combination of both. The proposed method is self-adaptive regulation by combining settings in P/Q or P/V mode with a setpoint decision making block (setpoint adaptation block) based on fuzzy logic in order to maintain a stable and optimal voltage profile.The simulation results obtained using Matlab®/Simulink® clearly show the efficiency of the controllers used and their optimal control capacity.
Comparison of Blind Source Separation Methods to Surface Electromyogram for Extensor Muscles of the Index and Little Fingers

Crosstalk is the result of the propagation of muscle electrical signals on surface electromyogram channels simultaneously. The objective of this paper is to study the behavior of three blind source separation (BSS) methods for crosstalk reduction during finger extensor muscle contractions: FastICA, joint diagonalization of covariance matrices and optimal filtering. These methods have been tested on artificial mixtures defined by a temporal sum of the real signals from isolated contraction of two independent biomechanical muscles for the extension of the index and little finger. Artificial mixtures display a ground truth for comparison between the methods. The separation was better using the optimal filtering compared to the other two methods. The optimal filtering have then be tested on real mixtures recorded during a simultaneous contraction of the two muscles. The results are less satisfactory but open doors to new perspectives.
A 17.7µW CDS-CTIA for Wireless-powered Wearable Electrochemical Sweat Sensors

A capacitor transimpedance amplifier (CTIA) for wireless-powered wearable electrochemical sweat sensors is designed and tested. Correlative double sampling (CDS) technology is utilized to suppress offset voltage and noise. Dedicated low-power and low-voltage designs meet the requirements of wireless-powered wearable applications where the power supply is limited, and voltage is unstable. The proposed CDS-CTIA is fabricated in 0.18-µm complementary metal oxide semiconductor (CMOS) process, occupying an active area of 0.0285mm2. The measured low-frequency gain is 33. 2MΩ/150. 4dBΩ under a 1.8V supply voltage, with a total power consumption of 17. 694µW (including 14.882µW static power and 2.812µW dynamic power). The input current ranges from -24nA to +19nA, and the input referred noise current is 7.76pArms in the 0.1-100 Hz frequency band. The proposed CDS-CTIA is capable of operating under a wide power supply ranging from 0.9V to 2.0V. In addition, its practicality is verified by measuring glucose concentration with an enzyme electrode.
Overview and Comparison of Methodologies for Design in Deep Submicron CMOS Processes

The paper makes an overview and comparison of three popular methodologies used for design and sizing of analog CMOS circuits in deep submicron technologies. The following methodologies are considered: design with models, based on the threshold voltage; design with simplified EKV models; design with the gmJID methodology. The goal of the paper is to evaluate the use of these methodologies and their accuracy when used for hand calculations in the early stage of the design. For this purpose, the application of these three methodologies is demonstrated in the design of current mirror OTA in 45nm CMOS technology. The results from the sizing and the simulation of the circuit are analyzed and summarized.
Internal Dosimetry in Diagnostic Nuclear Medicine Using Monte Carlo Techniques

Patient-specific internal dosimetry with high accuracy is the most significant issue in the field of nuclear medicine. In recent researches has been a dramatic alter in different methods to compute the correct organ doses according to injected radioactivity distribution, admittedly Monte Carlo simulation has brought high accuracy results in voxel-based dosimetry techniques.In this study patient datasets who were injected F18-FDG (2 subjects, normal cases, 60±8.2 kg, injected activity:10 ±0.5 Mbq) were acquired in 3 times sequences (30,60,90 min post-injection). The CT and PET images were used as attenuation maps and activity distribution of patient phantom respectively. Hence the CT and PET images have been registered by 3D Slicer software to achieve the same matrix and pixel sizes. The regions of interest were segmented on the CT images that entailed kidneys, spleen, bladder, lung, pancreas, liver, stomach, gallbladder organs. The segmenting ROI of CT images were reconstructed by MATLAB codes and the voxelized phantom and voxelized source of each patient at specific times were generatedBy way of conclusion, the F18-FDG dose distribution in patient-specific phantom has investigated and bladder, spleen, kidney absorbed the most activity as we expected by PET images, the mean S- factors and absorbed dose were computed as 4.02e-05 mGy/Mbq & 0.67±1.2 mGy, 5.29e-07 mGy/Mbq & 1.67±0.45mGy, 2.23e-07 mGy/Mbq & 0.57±0.52 mGy for bladder, kidney and spleen organs. Monte Carlo methods are shown the best results aspect of accuracy but the essential issue that prevents clinical uses is excessive computational cost and time.
Deep Metric Representation Learning for Clinical Resting State fMRI

With growing size of resting state fMRI datasets and advances in deep learning methods, there are ever increasing opportunities to leverage progress in deep learning to solve challenging tasks in neuroimaging. In this work, we build upon recent advances in deep metric learning, to learn embeddings of rs-fMRI data, which can then be potentially used for several downstream tasks. We propose an efficient training method for our model and compare our method with other widely used models. Our experimental results indicate that deep metric learning can be used as an additional refinement step to learn representations of fMRI data, that significantly improves performance on downstream modeling tasks.
Multimodal Emotion Recognition in Response to Oil Paintings

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.
Probabilistic Modeling of PHEV Load and Its Impacts in Unbalanced Distribution System with D-STATCOM

To minimize global warming effect and carbon emission impacts, plug-in hybrid electric vehicles (PHEVs) are becoming popular now-a-days as an alternative to conventional vehicles. As PHEVs are charged from existing grid, so planning and analysis should be done properly for integration. In this paper, the impact of assimilating PHEV in the distribution network in terms of all the technical factors such as voltage profile, line loss, voltage unbalance is analysed. Taking into account the uncertainty of PHEV load, the 24 hour power demand curve of PHEVs is developed which is probabilistic in nature. Then PHEVs are incorporated with residential load in the IEEE-13 bus system. Here the hourly load flow for unbalanced distribution system is performed in OpenDSS (version 9.4.0.1) software interfaced with MATLAB (version 2018a) software. It was observed that increase in penetration level of PHEVs leads to violation of security constraints in terms of voltage profile and voltage unbalance. The performance parameters are studied for different penetration levels of PHEVs. In order to increase the penetration level of PHEV in the distribution system, FACTS devices such as DSTATCOMs are incorporated in the existing system and the results were compared and analyzed.
Investigating the Effects of Anatomical Structures on the Induced Electric Field in the Brain in Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is capable of stimulating neurons in the brain non-invasively and provides numerous possibilities for the treatment of various neurological disorders such as major depressive disorder, Parkinson's disease, obsessive compulsive disorder. TMS coils can affect the distribution of induced electric fields significantly, thus the design of TMS coils is always a popular topic in TMS studies. Yet the importance of the role of anatomical structures in the induced electric field has not been thoroughly investigated. Therefore, this work has compared the strength of electric fields induced from fifty realistic head models with twelve commercial or novel TMS coils to explore how anatomical structures affect the electric field. It has been found that the electric field strengths among the fifty head models showed highly correlated patterns. The coils were placed at two positions, where all the twelve coils were placed at the vertex and eight of them were placed at the dorsolateral prefrontal cortex of the head due to the coil geometry. Notably, fifty heterogeneous head models that are derived from MRI data were used in the simulations for examining the difference on the performance of TMS coils caused by different anatomical structures. A total of one thousand simulations have been conducted, providing a large amount of data for analysis. Clinical Relevance- This provides a basis to make treatment protocols or predictions in TMS clinical trials considering the different anatomical structures among subjects.
Space-Time Independent Component Analysis of Brain Signals: Component Selection and the Curse of Dimensionality

Performing Independent Component Analysis (ICA) on biomedical signals is quite commonplace. ICA is usually applied to multi-channel data however not always with great success. In previous work we realized an innovation to standard ICA which we call space-time ICA (ST-ICA). This method brings into play both spatial and temporal/spectral information to perform very powerful extractions and overcomes the individual limitations of ensemble (spatial) ICA and single-channel (temporal) ICA. The cost in implementing ST-ICA is the curse of dimensionality since spatio-temporal analysis of multi-channel physiological data recorded at suitable sampling speeds results in large unwieldy datasets which become impossible to parse without any form of truncation or at least an automated component selection process. Here we address the component selection problem on the application of ST -ICA to real-world neurophysiological data-specifically in extracting seizure data from EEG recordings. We assess the information held in each of the spatio-temporal features resulting from ST-ICA and comment on the development of an efficient method to extract them, as well as using dimensional reduction techniques to reduce the curse of dimensionality resulting successful separation of meaningful physiological data from noisy, artifact laden datasets. Clinical Relevance-These methods will allow for the automatic identification and extraction of poorly defined episodes of physiologically meaningful activity in noisy multi-channel recordings of brain signals.