Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamic model of hand movements for proportional myoelectric control of a hand prosthesis*

In myo-controlled prosthetic hands, surface electromyographic signals are used to operate the hand actuators. A pre-requisite for effective control is that the intended movement is decoded from muscle activity. Simpler approaches use pattern recognition techniques, which assume a finite set of possible actions. However, this leads to unnatural, discontinuous control. Proportional controllers do not require a finite set of actions to be specified in advance but are difficult to use, particularly with dexterous multi-fingered hands. Here we discuss a control module which continuously predicts the intended movements from recorded multi-channel electromyographic activity. The module can be seen as a (simplified) forward model of the dynamics of the intact hand. We describe a procedure for estimating model parameters from hand movement and muscle activity data, and discuss its application to the proportional myoelectric control of a prosthetic hand.
The Treatment of Gerund Forms for Arabic Nouns with LKB System

: The treatment of morphological phenomena is important in Natural Language Processing (NLP), especially using a unification grammar. In Arabic grammar, the gerund is considered one of the most delicate morphological structures since it changes the grammatical category. Thus, we present in this paper, a Head-driven Phrase Structure Grammar (HPSG), treating Arabic gerund forms. The elaborated grammar is specified with Type Description Language (TDL) and validated on Linguistic Knowledge Building (LKB) system. The obtained results were encouraging, which proves the effectiveness of our system.
Influence of gender-related differences in transcranial direct current stimulation: A Computational Study*

Gender-related anatomical differences have been reported with respect to brain and cerebrospinal fluid. The cortical induced electric field (EF) in transcranial direct current stimulation (tDCS) depends both on the dose (electrode montage and applied current) and the individual anatomical details. Therefore in spite of fixed dose, one can expect gender-related differences to impact induced EF which in turn would influence tDCS outcome. The abundance of promise with tDCS in both research and clinical domains is also accompanied with variability in response. The in-constant induced cortical EF is one of the main contributors of this variability.The aim of this study is to quantify the effects of the gender-related morphological changes on tDCS induced cortical EF. MRI data were obtained for 10 healthy individuals (5 males: M1–M5 and 5 females: F1-F5) spanning ages 27-47 years. Finite element models derived from the individual MRI and simulating the classic left motor cortex–contralateral supraorbital (C3-SO) montage were used to predict the cortical EF. The percentage tissue volume were also determined to illustrate anatomical differences in the dataset considered.Findings indicate that induced EF is higher in female head models on an average than male head models across several metrics. While the average peak EF value in female head models was comparable to that of male head models, the mean and median values were 11.6% and 10% higher. On an individual basis, the highest peak value was observed in a female subject F3 (0.83 V/m) while the lowest peak value was observed in male subject M2 (0.34 V/m) -indicating a variation of ~2.4-fold across the dataset considered. The average gray matter percentage volume in females was 11.6% higher than in males. The average white matter percentage volume was 8.7% higher in females while negligible CSF percentage volume difference was noted across gender. The results of our study indicate gender-related differences in tDCS induced current flow and quantify the extent of this variation.
Exploring the supra linear relationship between PetCO2 and fMRI signal change with ICA

The relationships between brain functions and the respiratory system are complex. Disentangling brain activity related to CO2 changes from nonspecific vasoreactivity is a challenge when studying brain activity involved in the control of breathing with fMRI. In this work, we analyzed a dose dependent relationship between arterial CO2 levels and brain response. To accomplish this goal, we developed a gas administration protocol, together with multi-subject ICA and specific nonlinear post-processing analysis. Our results highlighted a supra-linear response to CO2 challenges in brainstem, thalamus and putamen. Results were discussed in the light of current knowledge about the central respiratory network.
A comparitive analysis of classification algorithms on different stimuli for EEG based BCI applications

Brain computer interface (BCI) is an interface that enables interaction between the human brain and peripheral devices for the restoration, assistance or augmentation of cognitive, sensory, motor and auditory events. This paper provides a comparative study on various classification algorithms to design and develop several BCI applications based on Electroencephalography(EEG) signals for the Event Related Potential (ERP) responses for auditory, somatosensory and cognitive events. In order to record the electrical activity of the brain, several auditory, cognitive, somatosensory tasks were given to the subjects. The present study evaluates the performance metrics of three classification algorithms namely k- nearest neighbors (KNN), Multi-Layer Perceptron(MLP) and Random Forest Classifier. This research is distinctive among its counterparts in this field of research in that this study compares the performance of different classifiers across the different Event Related Potential (ERP) responses for different stimuli.
Predicting Human Embryos' Implantation Outcome from a Single Blastocyst Image

Only one-third of embryo transfer cycles via invitro fertilization, the most common fertility treatment, leads to a clinical pregnancy. Identifying embryos with the highest potentials for transfer is an essential step to optimize in-vitro fertilization outcome. However, human embryos are complicated by nature and some of their developmental aspects has still remained a mystery to expert biologists. In this paper, the first-ever attempt is made to estimate probability of implantation using a single blastocyst image. First, a semantic segmentation system is proposed for human blastocyst components in microscopic images. Second, a multi-stream classification model is proposed for the prediction of embryos' implantation outcome. The proposed classification model features an architectural component, Compact-Contextualize-Calibrate (C3) to guide the feature extraction process and a slow-fusion strategy to learn cross-modality features. Experimental results confirm that the proposed method delivers the first-reported implantation outcome prediction via a single blastocyst image to date with a mean accuracy of 70.9%.
Influence of Showroom Visit Experience on Recommendation Intention in the Japanese Automotive Industry

For industries that use showrooms to exhibit products where Electronic Commerce (EC) is widespread, studies have led to the concept of “showrooming. ” Interestingly, the literature is largely silent regarding the evaluation of showrooms in industries where EC presence is low, such as the automobile industry. The style of showrooms in the automobile industry is diverse. In addition to ordinary exhibitions, showrooms may use other formats, such as theme parks, cafes, and boutiques. As a general principle, it is generally argued that brand experience affects future purchase factors more strongly than product information alone. This principle emphasizes the consumer experience, as it is considered to have a higher economic value than the products themselves. However, few studies have reported quantitative verification of these alleged showroom benefits. In this study, the influence of visiting various types of showrooms in the Japanese automobile industry, such as theme parks and cafes, was evaluated based on the recommendation intention of consumers. The tendency score, as a measure of the variables that may affect customer experience at a showroom, was evaluated using a Poisson regression model. Based on data obtained via an online survey, the model was adjusted by the propensity score as a measure of recommendation intention. We find that the showroom visit experience did not significantly affect the repurchase intention. Hence, further research is needed to understand how the showroom experience should be changed to generate results, that is, to ensure that the showroom experience positively influences recommendation intentions.
Effects of Hydrogel-Fiber on Cystic Cavity after Spinal Cord Injury

Spinal cord injury (SCI) affects millions of people around the world, however, functional recovery is far from satisfying. The continuous emergence of biomaterials provides a new idea for the repair of SCI. Hydrogels can mimic the extracellular matrix (ECM), however, the unstable hydrogel shape limits its application. In this study, we evaluate the effect of hydrogel fiber (Polycaprolactone, PCL fiber was added to the hydrogel) on the recovery after SCI. 20 adult male Wistar rats were randomly divided into 4 groups: SCI+hydrogel group (H), SCI+hydrogel + PCL fiber group (HF), SCI group (SCI) and SHAM group (SHAM) and (N=5). SCI contusion injury was induced by a MASCIS Impactor (20g weight, 50cm high) at the T9 level in rats. Hydrogels or PCL fiber were administered into the SCI site one week after surgery. Periodical Basso, Beattie, and Bresnahan (BBB) locomotor score, spinal cord hematoxylin and eosin stain (HE) staining, and immunofluorescence staining were performed 28 days after the operation. HE staining showed that the average cystic cavity area in SCI (20.78 ±2.93 mm2) group was significantly higher than that in H group (6.54 ±0.85 mm2), HF group (5.06 ±0.76 mm2) and SHAM group (1.76 ±0.27 mm2) (P < 0.001). There was no significant difference in BBB motor score among the HF group (16.80±1.10), SCI (14.20±1.09) and H group (15.00±1.23) (P > 0.05), except the sham group. Immunofluorescence showed higher NeuN positive cells in both the H group and the HF group. This preliminary result may indicate that PCL fiber optimized the strength of hydrogels, thus providing better support for the axon regeneration. Future investigation is needed to further characterize PCL fiber and elucidate related mechanisms.
PREgDICT : Early prediction of gestational weight gain for pregnancy care

Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.
Laser ablation in biliary tree: analysis of the intraductal and superficial thermal effects during the treatment

The treatment of choice for the unresectable cholangiocarcinoma is based on biliary decompression procedures. Despite stent placement is the standard of care, it is related to well-known complications. Hence, alternative techniques were proposed. Ideally, they should guarantee an adequate intraductal disobstruction, without injuring the surrounding tissues.This pre-clinical study aims to investigate the thermal effects of the laser ablation (LA) in the biliary tree, in terms of intraductal and surrounding tissue temperature achieved with different laser settings. The common bile ducts (in their upper and lower portions) of two pigs were ablated for 6 minutes with a diode laser at 3 W and 5 W. A custom-made laser applicator was used to obtain a circumferential ablation within the ducts. The intraductal temperature (Tid) was monitored by means of a fiber Bragg grating (FBG) sensor, while an infrared thermal camera monitored the T distribution in the surrounding tissues (Tsup). A maximum T difference of 65 °C and 57 °C was evidenced between the two power settings for the Tid measured in the upper and lower ducts, respectively. The mean difference between Tid and the averaged Tsup values was evaluated. At 5 W, a difference of 37±3 °C and 44±10 °C were obtained for the upper and lower ducts, respectively. At 3 W, a T difference of 2±1 °C was obtained for the upper biliary duct, while a difference of 8±1 °C was documented for the lower duct. Based on the results obtained in this preliminary study, the possibility to equip the laser probe with temperature sensor can improve the control and the safety of the procedure; this solution will guarantee the monitoring of the treatment while preserving the lumen and the surrounding structures.
Managing Ferroresonance Overvoltages in Distribution Systems

Overvoltages resulting from ferroresonance have been a hurdle to utilities for several decades. Ferroresonance is frequently difficult to diagnose as its development and effects are often surprising. This paper presents qualitative and quantitative approaches, and a theoretical background on how ferroresonance develops, as well as a guideline on how to manage the extent of its effect. The paper also presents a case study experienced by a Canadian electric utility company. The goal is to aid utility staff by presenting its experience on how to understand and diagnose the phenomenon.
Lateralization of processing spectrally-degraded music in the auditory cortex: An fNIRS study

Music processing is one of the most complex cognitive activities that human brain performs. The mechanism of music processing when musical sounds are perceived by listeners fitted with a cochlear implant (CI) is not well understood yet. The present study examined the effect of spectrally-degrading processing (via a noise-vocoding processing to simulate CI speech processing) on the hemispheric lateralization in music processing using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses in both hemispheres caused by the perception of the original, 32-channel noise-vocoded and 16-channel noise-vocoded musical sounds were measured using fNIRS. The right-hemispheric lateralization in the original, 32-channel noise-vocoded and 16-channel noise-vocoded music processing was about 72%, 67%, 56% of all participants, respectively. The activation level of the auditory cortex caused by the perception of the original music was higher than that of the noise-vocoded music, and the activation level reduced when decreasing the number of channels in the noise-vocoder processing. The activation levels in the right auditory cortex in all conditions were higher than those in the left auditory cortex; however, the difference of the contrast values between the right and left hemispheres reduced when decreasing the number of channels in the noise-vocoder processing. Results in this work indicated that the spectrally-degrading processing in CI speech processing may diminish the dominant role of the right hemisphere in music processing.
Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review

Human Activity Recognition using embedded sensors has lately made renowned development and is drawing growing attention in numerous application domains including machine learning, pattern recognition, context awareness, and human-centric sensing. Due to the lacking of a prominent analysis of this topic that can acquaint concomitant communities of the research avant-garde, there are still vital perspectives that, if pleaded, would create a vital turn in the way of interaction among people and mobile devices. In this paper, we have presented a comprehensive survey along with the prevailing state of various challenges of human activity recognition based on wearable, environmental, and smartphone sensors. Firstly, we have shown numerous factors to be considered for the data pre-processing part regarding noise filtering and segmentation methods. Besides, we have made a list of sensing devices, sensors, and applications that can be used for collecting activity data along with a discussion on sensor position and requirements. Moreover, we have made a comprehensive analysis of some benchmark datasets, which includes information about sensors, attributes, activity classes, etc. Finally, we have shown an analysis of activity recognition approaches on some of the benchmark datasets based on existing works.
Extraction of Rice-planted Area based on MobileUnet Model and Radarsat-2 Data

Extracting rice-planted area based on Synthetic aperture radar (SAR) image can effectively solve the cloud pollution problems of optical images. Compared with the traditional image classification method, deep learning method based on semantic segmentation algorithm can effectively utilize the information that can represent the image features and backscatter difference between different ground objects in remote sensing data, which satisfies the need of rapid and accurate extraction of ground information in the field. In this paper, the extraction of rice-planted area was implemented using MobileUnet model and rice label datasets were created using Radarsat-2 quad-polarized backscattering coefficient images and Freeman-Durden decomposition images obtained during the rice growth period in 2016. The results of our experiment showed that the Precision, Recall and MIoU are 0.964, 0.962 and 0.826 respectively. We analyzed the results and proposed future research prospects finally.
A Correlation-Based Algorithm for Beat-to-Beat Heart Rate Estimation from Ballistocardiograms

Ballistocardiography (BCG) is a type of non-contact measurement technique that measures the mechanical reaction of the body resulting from heart contraction and the subsequent cardiac ejection of blood. Herein, we present an algorithm for beat-to-beat heart rate estimation from BCG signals that is both highly universal and easy to implement. The algorithm is based on the correlation between heartbeats in the same section of BCG. It first generates patterns by autocorre-lation, which are then matched with the remaining signals to determine heartbeats. The agreement of the proposed algorithm with synchronized electrocardiogram has been evaluated, and a relative beat-to-beat interval error of 1.66% and a relative average heart rate error of 1.25% were observed. The proposed algorithm is a promising candidate for a non-contact, long-term cardiac monitoring system at home.
Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator

Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selection Operator (LASSO) to broaden the estimation of brain connectivity to those conditions in which current methods fail due to the limited data points available. We tested the performances of the LASSO regression in a simulation study under different levels of data points available, in comparison with a classical approach based on OLS and AS. Then, the two methods were applied to real electroencephalographic (EEG) signals, recorded during a motor imagery task. The simulation study and the application to real EEG data both indicated that LASSO regression provides better performances than the currently used methodologies for the estimation of brain connectivity when few data points are available. This work paves the way to the estimation and assessment of connectivity patterns with limited data amount and in on-line settings.
Effects of Gastrin-releasing Peptide on Hippocampal Neural Networks in Vascular Dementia Rats*

Gastrin-releasing peptide (GRP) has been confirmed to exhibit a variety of physiological functions in the brain and play a role in many neurological diseases. Our previous research found that GRP could restore the impaired synaptic plasticity and the spatial learning and memory impairments induced by vascular dementia (VD). However, the specific mechanisms of GRP affecting hippocampus, especially the effects on the neuronal oscillations were still poorly understood. In this study, we examined the effects of GRP on the changes of the interactions between theta and gamma oscillations in the hippocampal CA3-CA1 pathway of VD rats and explored the potential electrophysiological mechanism. To this purpose, local field potentials (LFPs) simultaneously collected from hippocampal CA3 and CA1 were measured by the power spectrum, phase synchronization, phase-phase coupling (PPC) and phase-amplitude coupling (PAC). We found that GRP substantially restored the phase synchronization of the theta and gamma oscillations. The GRP also significantly improved the strength of theta-gamma cross-frequency coupling (including theta-gamma PPC and theta-gamma PAC) in the CA3-CA1 network. The results indicated that GRP could alleviate the changes of neural activities in hippocampal CA3-CA1 pathway induced by VD. This might be an electrophysiological mechanism for GRP preventing cognitive impairments induced by VD.
Optimized Transcutaneous Spinal Cord Direct Current Stimulation using Multiple Electrodes from 3/9/7 System

Transcutaneous spinal cord direct current stimulation (tSDCS) has been applied as an easy non-invasive approach to modulate spinal cord functions. Currently there is no formal layout or guidelines for electrode placement to optimize tSDCS. Most clinical applications simply place the stimulating electrode over the intended spinal cord target. Here we show that this ad hoc method cannot achieve optimal stimulation. Specifically, we propose a new electrode layout for optimized tSDCS. The candidate high-definition electrodes distribute on the back of the body evenly and the layout was named 3/9/7 system. Algorithmic optimization was performed leveraging this electrode placement system and a 1 mm3 human full body model. Results show that the optimal stimulation montages cannot be trivially determined and they outperform the unoptimized stimulation configuration. This work opens the possibility for systematic treatment planning in future clinical applications of tSDCS.