Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-subject EEG-based Emotion Recognition Using Adversarial Domain Adaption with Attention Mechanism

Cross-subject EEG-based emotion recognition (ER) is a rewarding work in real-life applications, due to individual differences between one subject and another subject. Most existing studies focus on training a subject-specific ER model. However, it is time-consuming and unrealistic to design the customized subject-specific model for a new subject in cross-subject scenarios. In this paper, we propose an Adversarial Domain Adaption with an Attention Mechanism method for EEG-based ER, namely ADAAM-ER, to decrease the individual discrepancy. ADAAM-ER consists of a Graph Convolution Neural Networks with CNNs (GCNN-CNNs) and an Adversarial Domain Adaption with a Level-wise Attention Mechanism (ADALAM). Specifically, GCNN-CNNs as a feature extractor, which constructs a broader feature space, is designed to obtain more discriminative features. And ADALAM, which can decrease the individual discrepancy by alignment of the more transferable feature regions, is introduced to further obtain the discriminative features with higher transferability. Consequently, the proposed ADAAM-ER method can design a more transferable emotion recognition model with more discriminative features for a new subject via improving transferability. Experimental results on the SEED dataset have verified the effectiveness of the proposed ADAAM-ER method with the mean accuracy of 86.58%.
Hess Screen Revised: How Eye Tracking and Virtual Reality change Strabismus Assessment

Strabismus is a visual disorder characterized by eye misalignment. The extent of ocular misalignment is denoted as the deviation angle. With the advent of Virtual Reality (VR) Head-Mounted-Displays (HMD) and eye tracking technology, new possibilities measuring strabismus arise. Major research addresses the novel field of VR strabismus assessment by replicating prism cover tests while there is a paucity of research on screen tests. In this work the Hess Screen Test was implemented in VR using a HMD with eye tracking for an objective measurement of the deviation angle. In a study, the functionality was tested and compared with a 2D monitor-based test. The results showed significant differences in the measured deviation angle between the methods. This can be attributed to the type of dissociation of the eyes.
Single-Trial Detection of Event-Related Potentials with Integral Shape Averaging: An Application to the Elusive N400

The estimation of Event-Related Potentials (ERPs) from the ambient EEG is a difficult task, usually achieved through the synchronous averaging of an extensive series of trials. However, this technique has some caveats: the ERPs have to be strictly time-locked with similar shape, i.e. emitted with the same latency and the same profile, with minor fluctuations of their amplitudes. Also, the method requires a huge number of valid trials (~100) to efficiently raise the ERPs from the EEG trials. In the case of cognitive ERPs, as with the N400, the delivered stimulus has to be different for each trial, the latencies are varying, and the number of available trials is usually low. In this paper, an alternative method, coined Integral Shape Averaging (ISA) and its derivatives are detailed. ISA is robust to varying latencies and affine transforms of shape. Furthermore, a new method coined ISAD can be derived to extract ERPs even from a single trial experiment. The aim here is to illustrate the potential of ISAD for N400 component extraction on real EEG data, with emphasis on its general applicability for ERPs computation and its major assets like reduced experimental protocol. Some insights are also given on its potential use to study ERP variability, through shape and latency.Clinical Relevance— The proposed algorithm aims to be a helpful tool in clinical practice to analyze and interpret evoked responses in real experimental settings, especially for particularities in neurology.
Optimization & Characterization of Interdigitated Electrodes for Microbial Growth Monitoring

This study optimally designed and implemented highly sensitive microscale interdigitated electrodes (IDEs) to monitor microorganisms' growth in diverse environments. Gold interdigitated electrodes (AuIDE) with 4 mm×4 mm effective sensing area and varying microscale interdigitate gaps were designed and fabricated. The electrodes were electrically characterized voltametrically. Electrochemical impedance spectroscopy (EIS) measurements were conducted to determine the optimal geometry by observing the impedance spectra of microelectrodes through varying pH and temperature. Furthermore, the sensors sensitivity was evaluated by measuring the impedance properties of a microscale volume of microorganism concentrations in growth media solution.
A Real-Time Algorithm to Estimate Shoulder Muscle Fatigue Based on Surface EMG Signal For Static and Dynamic Upper Limb Tasks

Despite prevention efforts, the prevalence of workrelated upper extremity musculoskeletal disorders (WRUED) is increasing. A limit in the development of preventive interventions is the lack of devices that can measure and process sEMG signals in order to provide real-time reliable information on muscular fatigue of the upper limb in relation to the physical demands of the work. In this paper, the development and evaluation of a real-time muscle fatigue detection algorithm based on sEMG will be presented. The proposed algorithm uses the median frequency of sEMG power spectrum density (PSD) obtained with the Continuous Wavelet Transform (CWT) as an indicator of the muscle fatigue level. To extend the algorithm’s efficiency to dynamic tasks, a muscle contraction detection module is added in order to remove the segments when the muscle is not contracting. To assess the algorithm’s performance, eight healthy adults performed simple static and dynamic shoulder tasks using different loads. The results of the proposed time-frequency method (i.e. CWT) were first compared to those of the traditional Short Time Fourier Transform (STFT). It was shown that the CWT performs better than the STFT in both static and dynamic loading conditions. The validity of the algorithm’s output as a muscle fatigue indicator was verified by comparing the output’s decrease rate with different loads. As expected, the algorithm’s fatigue indicator decreased faster over time with heavier loads. It was also shown that the initial muscle fatigue estimation output is independent of the load. Finally, we studied the proposed muscle contraction detection module’s efficiency to overcome issues associated with dynamic tasks. We observed a substantial improvement of the smoothness of the fatigue indicator’s evolution by using of the muscle contraction detection module.
Assessing Arousal Through Multimodal Biosignals: A Preliminary Approach

The increase in Autism Spectrum Disorder (ASD) prevalence estimates over the last decades has driven a quest to develop new forms of rehabilitation that can be accessible to a larger part of this population. These rehabilitation approaches often take the form of computer games that are blind to the user’s emotional state, which compromises their efficacy. In this study, a set of physiological signals were acquired in simultaneous with functional Magnetic Resonance Imaging (fMRI) with the future prospect of combining both kinds of data to create models capable of assessing the true emotional state of their users based on physiological response as a measure of autonomic nervous system, having as ground truth the activity of targeted brain regions. This paper describes an initial approach, focusing on the information contained on the physiological signals alone. A total of 35 features were extracted from biosignals’ segments and subsequently used for automatic classification of arousal state (High Arousal vs. Low Arousal). The suboptimal results, although some extracted features present statistically significant differences, underline the challenging nature of our proposal and the added obstacles of recording physiological signals in the magnetic resonance environment. Further exploration of the measured signals is needed to gather a bigger number of discriminative features that can improve classification outcomes.
Effects of denoising strategies on R-wave detection in ECG analysis

The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace, which can be characterised through its peaks and troughs. The signal can then be used by clinicians to diagnose cardiac disorders. However, as with any measuring equipment, the ECG output signal can experience deterioration resulting from noise. This can happen due to environmental interference, human issues or measuring equipment failure, necessitating the development of various denoising strategies to reduce, or remove, the noise. In this paper, we study typically occurring types of noise and implement popular strategies used to rectify them. We also show, that the given strategy’s denoising potential is directly related to R-wave detection, and provide best strategies to apply when faced with specific noise type.
A new generator static excitation system using boost-buck chopper

A new generator excitation system using a boost-buck chopper as a kind of static exciter is proposed to overcome the lack of field forcing capability of the bus fed exciter under the input line fault condition. It increases or maintains the generator field current by boosting the field voltage in the case of the input AC line voltage drop during and immediately after a fault. The validity of the proposed excitation system is verified with the computer simulation. The generator stability according to different exciters is tested using a commercial software package-CYME. The simulation results of the stability analysis on the generator with the proposed exciter is better than that of the bus fed exciter. This boost-buck chopper exciter can be simply implemented and controlled by the modern power electronics technology.
Efficient J Peak Detection From Ballistocardiogram Using Lightweight Convolutional Neural Network*

Ballistocardiagram (BCG) is a non-contact and non-invasive technique to obtain physiological information with the potential to monitor Cardio Vascular Disease (CVD) at home. Accurate detection of J-peak is the key to get critical indicators from BCG signals. With the development of deep learning methods, many researches have applied convolution neural network (CNN) and recurrent neural network (RNN) based models in J-peak detection. However, these deep learning methods have limitations in inference speed and model complexity. To improve the computational efficiency and memory utilization, we propose a robust lightweight neural network model, called JwaveNet. Moreover, in the preprocessing stage, J-peaks are re-modeled by a new transformation method based on their physiological meaning, which has been proven to increase performance. In our experiment, BCG signals, including four different sleeping positions, were collected from 24 subjects with synchronous electrocardiogram (ECG) signals. The experiment results have shown that our lightweight model greatly reduces latency and model size compared to other baseline models with high detecting accuracy.
Abstract. Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea

Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter. In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep: wake (W), rapid eye movement (REM), and nonREM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen’s kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home
Analysis of the series resonant converter using a frequency domain model

The steady-state analysis of the series resonant converter with a capacitive output filter is studied using a frequency-domain model. The expressions for the resonant current and the capacitor voltage are derived. The converter shows multiple continuous conduction mode with continuous gate/base drive pulses. The performance characteristics relating to output voltage, output current, switching frequency, and feedback diode conduction angle are given. The input power, output power, and efficiency of the series resonant converter with finite quality factor are included. Some guidelines in the selection of component values and their ratings are given with an illustrative example. <<ETX>>
EEG representation approach based on Kernel Canonical Correlation Analysis highlighting discriminative patterns for BCI applications

Brain-Computer Interface (BCI) is applied in the study of different cognitive processes or clinical conditions as enhancing cognitive skills, motor rehabilitation, and control. However, many approaches focus on using a robust classifier instead of providing a better feature space. This work develops a feature representation methodology through the kernel canonical correlation analysis to reveal nonlinear relations between filter-banked common spatial patterns (CSP) extracted. Our approach reveals nonlinear relations between ranked filter-banked multi-class CSP features and the labels in a finite-dimensional canonical space. We tested the performance of our methodology on the BCI Competition IV dataset 2a. The introduced feature representation using a classic linear SVM achieves accuracy rates competitive with the state-of-the-art BCI strategies. Besides, the processing pipeline allows identifying the spatial and spectral features driven by the underlying brain activity and best modeling the motor imagery intentions.Clinical relevance— This BCI strategy assesses the nonlinear relationships between time series to improve the interpretation of brain electrical activity, taking into account the spatial and spectral features driven by the underlying brain dynamic.
202 1 1 If you Say that Idioms Should be Learned , then Why don ’ t you Teach them ? A Case Study of In-service EFL Teachers 1

Recent communicative teaching approaches view learning English as a Foreign Language (EFL) as involving more than knowledge of grammar structures. Students are expected to develop communicative skills which allow them to interact inside and outside the language classroom. To this end, learning idioms plays a significant role because these expressions improve students’ proficiency, fluency and native-like performance (Boers et al., 2006). However, even though they are beneficial for students’ language skills, some teachers may decide to avoid teaching these expressions in the classroom. Motivated by this, the present study examines the perceptions of five Mexican EFL teachers concerning the teaching of English idioms. Semi-structured interviews reveal that the five teachers perceive that teaching idioms is highly important for developing students’ communicative competence. However, the results also indicate that four teachers avoid teaching them because of institutional demands to cover a textbook and their perceptions of their students’ low proficiency levels. This evidence suggests that preand in-service teacher education programs should provide teachers with a better understanding of strategies that they can use to teach idioms.
A Chiral fNIRS Spotlight on Cerebellar Activation in a Finger Tapping Task

Functional Magnetic Resonance Imaging (fMRI) has been so far the golden standard to study the functional aspects of the cerebellum. In this paper, a low-cost alternative imaging, i.e. functional Near-Infrared Spectroscopy (fNIRS) is demonstrated to achieve successful measurements of the cerebellar hemodynamics towards the challenging observation of motor and cognitive processes at the cerebellar level. The excitation and reception optodes need to be properly placed to circumvent a major hindering from the shielding by the neck muscles. A simple experimental protocol, i.e. finger tapping task, was implemented to observe the subject’s engagement and the presence of functional asymmetries. Marked differences among subjects with different levels of lateralization were clearly noticed in terms of activation and latencies, together with peaks in the hemodynamic response following neural activation. These preliminary results suggest also differences in the hemodynamic behavior between the brain and the cerebellum and encourage future and extended analysis in this direction.Clinical Relevance—This establishes the possibility to use a novel technique (fNIRS) to study cerebellar hemodynamics instead of fMRI.
Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks

Cardiac Auscultation, an integral part of the physical examination of a patient, is essential for early diagnosis of cardiovascular diseases (CVDs). The ability to accurately diagnose the heart sounds requires experience and expertise, which is lacking in doctors in the early years of clinical practice. Thus, there is a need for an automatic diagnostic tool that would aid medical practitioners with their diagnosis. We propose novel hybrid architectures for classification of unsegmented heart sounds to normal and abnormal classes. We propose two methods, with and without the conventional feature extraction step in the classification pipeline. We demonstrate that the F score using the approach with conventional feature extraction is 1.25 (absolute) more than using a baseline implementation on the Physionet dataset. We also introduce a mechanism to tag predictions as unsure and compare results with a varying threshold.
The Home Market Effect in a Home Biased Geography

A demand-side mechanism for international trade, the Home Market Effect (HME), predicts a more-than-proportional relationship between domestic expenditure and domestic production. Yet, since its inception in the 1980s by Paul Krugman, this theoretical result has only been shown to be generally valid in two-location models. I prove that the HME is maintained in an arbitrary number of locations provided the geography of trade is home-biased: the majority of domestic sales go to domestic consumers. Intuitively, without home bias, increasing domestic expenditure can actually benefit foreign production more, thus causing domestic production to rise by less, violating the more-than-proportional relationship. This result has been overlooked until now because in standard two location models all geographies are necessarily home-biased. ∗New York University Abu Dhabi, jjnorris@nyu.edu. I thank Jiacheng Li for really excellent research assistance.
Learning Control Policies in Smart Cities from Physical Data

Motor vehicle emissions are the primary contributors to the increase in ambient pollution levels. Rapid urbanization and lack of a good solution to manage the traﬃc are forcing cities to take drastic measures against the automotive industry. In this thesis, we build a case study of the traﬃc in the Norwegian city of Trondheim and create a realist simulation based on real world data, that simulates the traﬃc and the emissions. We then propose a Reinforcement Learning based solution that controls the access to the diﬀerent regions of the city to optimize the traﬃc given a desired metric. We also take a look at diﬀerent improvements, like using a multi-agent system and using pre-generated data for the training phase. We compare the obtained results with the baseline and against a reactive agent. At the end we assess the solution’s strengths and weaknesses, and propose possible future improvements.
Bottom of the Pyramid Innovation in Asia

The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the Asian Development Bank (ADB), its Board of Governors, or the governments they represent. The ADB does not guarantee the accuracy of the data included in this document and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned.
Evaluating annual and seasonal patterns of suspended sediment loads in a semi-arid watershed, central Algeria

Soil loss is a major problem for watersheds management in semi-arid environments. The objective of the present study is to analyze the annual and seasonal patterns of suspended loads and quantify the specific sediment yields in a semi-arid environment of the Mazafran Watershed in central Algeria. The obtained information of water discharge and suspended sediment load, recorded during 19 years, was confronted with precipitation data in order to establish the relationships between the forcing agents and erosive processes. The specific sediment yield was estimated by assessing rating curve data under two types of identified responses. The obtained results allowed confirming the seasonality on suspended sediment transport in the studied basin, which accounts for 56% of the total suspended sediment load estimated in winter. The mean annual suspended sediment is estimated at 17.52 Mg∙ha·y. The results highlighted that the type 2 event dominates the production of sediment in the study area in comparison with type 1 event. The analysis of the variability of rainfall erosivity index showed that there is a strong correlation between the annual precipitation and modified Fournier index (MFI), and a weak correlation with the monthly precipitation concentration index (PCI). Moreover, the spatial distribution of the modified Fournier index at the basin scale showed the highest precipitation aggressiveness in the Southern part of the study region for both type of events, whereas the precipitation aggressiveness low to moderate in the remaining part of the study region.