Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Related Systemic Artifacts in Functional Near-Infrared Spectroscopy*

Functional near-infrared spectroscopy (fNIRS) has the potential to become the next common noninvasive neuroimaging technique for routine clinical use. Compared to the current standard for neuroimaging, functional magnetic resonance imaging (fMRI), fNIRS boasts several advantages which increase its likelihood for clinical adoption. However, fNIRS suffers from an intrinsic interference from the superficial tissues, which the near-infrared light must penetrate before reaching the deeper cerebral cortex. Therefore, the removal of signals captured by SS channels has been proposed to attenuate the systematic interference. This study aimed to investigate the task-related systemic artefacts, in a high-density montage covering the sensorimotor cortex. We compared the association between LS and SS channels over the contralateral motor cortex which was activated by a hand clenching task, with that over the ipsilateral cortex where no task-related activation was expected. Our findings provide important guidelines regarding how to removal SS signals in a high-density whole-head montage.
The Effect of Longitudinal Training on Working Memory Capacities: An Exploratory EEG Study

The study of working memory (WM) is a hot topic in recent years and accumulating literatures underlying the achievement and neural mechanism of WM. However, the effect of WM training on cognitive functions were rarely studied. In this study, nineteen healthy young subjects participated in a longitudinal design with one week N-back training (N=1,2,3,4). Experimental results demonstrated that training procedure could help the subjects master more complex psychological tasks when comparing the pre-training performance with those post-training. More specifically, the behavior accuracy increased from 68.14±9.34%, 45.09±14.90%, 39.12±12.71%, and 32.11±10.98% for 1-back, 2-back, 3-back and 4-back respectively to 73.52±4.01%, 69.14±5.28%, 69.09±6.41% and 64.41±5.12% after training. Furthermore, we applied electroencephalogram (EEG) power and functional connectivity to reveal the neural mechanisms of this beneficial effect and found that the EEG power of δ, θ and α band located in the left temporal and occipital lobe increased significantly. Meanwhile, the functional connectivity strength also increased obviously in δ and θ band. In sum, we showed positive effect of WM training on psychological performance and explored the neural mechanisms. Our findings may have the implications for enhancing the performance of participants who are prone to cognitive.
Time Domain And Frequency Domain Heart Rate Variability Analysis on Gyrocardiograms

Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. The interest in heart rate monitoring without electrodes led to the rise of alternative heart beat monitoring methods, such as gyrocardiography (GCG). The purpose of this study was to compare HRV indices calculated on GCG and ECG signals. The study on time domain and and frequency domain heart rate variability analysis was conducted on electrocardiograms and gyrocardiograms registered on 29 healthy male volunteers. ECG signals were used as a reference and the HRV analysis was performed using PhysioNet Cardiovascular Signal Toolbox. The results of HRV analysis show great similarity and strong linear correlation of HRV indices calculated from ECG and GCG indicate the feasibility and reliability of HRV analysis based on gyrocardiograms.
Face Anti-Spoofing With Deep Neural Network Distillation

One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain. We propose to leverage data from a richer and related domain to learn meaningful features through the concept of neural network distilling. We first train a deep neural network based on reasonably sufficient labeled data in an attempt to “teach” a neural network for the application-specific domain for which training samples are scarce. Subsequently, we form training sample pairs from both domains and formulate a novel optimization function by considering the cross-entropy loss, as well as maximum mean discrepancy of features and paired sample similarity embedding for network distillation. Thus, we expect to capture spoofing-specific information and train a discriminative deep neural network on the application-specific domain. Extensive experiments validate the effectiveness of the proposed scheme in face anti-spoofing setups.
Intrinsic Complexity of Sympathetic and Parasympathetic Dynamics from HRV series: a Preliminary Study on Postural Changes

The analysis of complex heartbeat dynamics has been widely used to characterize heartbeat autonomic control in healthy and pathological conditions. However, underlying physiological correlates of complexity measurements from heart rate variability (HRV) series have not been identified yet. To this extent, we investigated intrinsic irregularity and complexity of cardiac sympathetic and vagal activity time series during postural changes. We exploited our recently proposed HRV-based, time-varying Sympathetic and Parasympathetic Activity Indices (SAI and PAI) and performed Sample Entropy, Fuzzy Entropy, and Distribution Entropy calculations on publicly-available heartbeat series gathered from 10 healthy subjects undergoing resting state and passive slow tilt sessions. Results show significantly higher entropy values during the upright position than resting state in both SAI and PAI series. We conclude that an increase in HRV complexity resulting from postural changes may derive from sympathetic and vagal activities with higher complex dynamics.
Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals

Every year, millions of patients regain conscious- ness during surgery and can potentially suffer from post-traumatic disorders. We recently showed that the detection of motor activity during a median nerve stimulation from electroencephalographic (EEG) signals could be used to alert the medical staff that a patient is waking up and trying to move under general anesthesia [1], [2]. In this work, we measure the accuracy and false positive rate in detecting motor imagery of several deep learning models (EEGNet, deep convolutional network and shallow convolutional network) directly trained on filtered EEG data. We compare them with efficient non-deep approaches, namely, a linear discriminant analysis based on common spatial patterns, the minimum distance to Riemannian mean algorithm applied to covariance matrices, a logistic regression based on a tangent space projection of covariance matrices (TS+LR). The EEGNet improves significantly the classification performance comparing to other classifiers (p- value < 0.01); moreover it outperforms the best non-deep classifier (TS+LR) for 7.2% of accuracy. This approach promises to improve intraoperative awareness detection during general anesthesia.
On the Selection of Neural Network Architecture for Supervised Motor Unit Identification from High-Density Surface EMG*

In the last decade, accurate identification of motor unit (MU) firings received a lot of research interest. Different decomposition methods have been developed, each with its advantages and disadvantages. In this study, we evaluated the capability of three different types of neural networks (NNs), namely dense NN, long short-term memory (LSTM) NN and convolutional NN, to identify MU firings from high-density surface electromyograms (HDsEMG). Each type of NN was evaluated on simulated HDsEMG signals with a known MU firing pattern and high variety of MU characteristics. Compared to dense NN, LSTM and convolutional NN yielded significantly higher precision and significantly lower miss rate of MU identification. LSTM NN demonstrated higher sensitivity to noise than convolutional NN.Clinical Relevance–MU identification from HDsEMG signals offers valuable insight into neurophysiology of motor system but requires relatively high level of expert knowledge. This study assesses the capability of self-learning artificial neural networks to cope with this problem.
Simulation-based Analysis of Magnetogastrography Sensor Configurations for Characterizing Gastric Slow Wave Dysrhythmias*

The routine diagnosis of gastric motility disorders represents a significant problem to current clinical practice. Magnetogastrography (MGG) provides a non-invasive option for assessing gastric slow wave (SW) dysrhythmias that are associated with motility disorders. However, its ability to characterize SW propagation is impaired by the limited spatial coverage of existing superconducting quantum interference devices (SQUIDs). Recently developed optically-pumped magnetometers can potentially substitute SQUIDs and enable subject-specific MGG arrays with greater spatial coverage. This study developed simulations of gastric MGG to determine the distribution of the magnetic fields (MFs) generated by SWs above the torso, and investigated the impact of several realistic dysrhythmic patterns of propagation. The distribution of MFs was found to vary significantly for different patterns of SW propagation, with ectopic dysrhythmia displaying the greatest difference from normal. Notably, some important proportion of the MFs lay outside the coverage of an existing experimental SQUID array used in gastrointestinal research for some simulated SW propagation patterns, such as retrograde activity. Results suggest that MGG measurements should be made over the entire frontal face of the torso to capture all of the strongest MFs generated by SWs.Clinical relevance— This provides a guide for the placement of MGG sensors for the capture of both normal and dysrhythmic gastric slow wave propagation.
Towards Controlling Functionally-Distinct Retinal Ganglion Cells In Degenerate Retina

Present retinal neuroprostheses have limited performance capabilities due to indiscriminate activation of different neural pathways. Based on our success in differentially activating ON and OFF cells using high frequency stimuli in a healthy retina, in this study we explored whether we could achieve similar differential activation between these two cell types but in degenerate retina. We found that after blocking the synaptic network, ON retinal ganglion cells (RGCs) could be differentially activated at higher frequencies (4 – 6 kHz) and amplitudes (200 – 240 µA), and OFF RGCs at relatively lower amplitudes (80 – 160 µA) across all tested frequencies. We further found that both cell types could be controlled by quickly modulating the frequency using short stimulation bursts. This work takes us one step closer to reducing the likelihood of indiscriminate activation of RGCs by accurately controlling the activation of functionally-distinct neural pathways.
SBL-Based Hybrid Precoder/ Combiner Design for Power and Spectrally Efficient Millimeter Wave MIMO Systems

This work proposes a novel sparse Bayesian learning (SBL)-based hybrid precoder/ combiner design scheme for millimeter wave (mmWave) MIMO systems. Towards this end, a multiple measurement vector (MMV) based sparse signal recovery problem is developed that maximizes the mutual information by approximating the hybrid precoder to the ideal digital precoder. A unique aspect of the proposed SBL-based scheme is that the resulting hyperparameter estimates can be used to activate the minimum number of RF chains required to approximate the ideal digital precoder/ combiner, thus enabling one to leverage the time-varying multipath profile of the underlying mmWave MIMO channel. This feature coupled with the improved ability of SBL for sparse signal recovery leads to a significantly enhanced power and spectral efficiency of the proposed scheme in comparison to the conventional schemes that activate a fixed number of RF chains and data streams, irrespective of the multipath profile of the mmWave MIMO channel. Simulation results demonstrate the improved efficiency of the proposed scheme in comparison to the existing schemes and also the resulting reduction in the average number of RF chains employed.
A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the existence of speckling noise, synthetic aperture radar image segmentation is a challenging task. We present a new method for synthetic aperture radar image segmentation in this article. Due to the large size of the original synthetic aperture radar image, we first divide the input image into small slices. Then the image slices are input to the attention-based fully convolutional network for obtaining the segmentation results. Finally, the fully connected conditional random field is adopted for improving the segmentation performance of the network. The innovations of our method are as follows: 1) The attention-based fully convolutional network is embedded with the multiscale attention network which is capable of enhancing the extraction of the image features through three strategies, namely, multiscale feature extraction, channel attention extraction, and spatial attention extraction. 2) We design a new loss function for the attention fully convolutional network by combining Lovasz-Softmax and cross-entropy losses. The new loss allows us to simultaneously optimize the intersection over union and the pixel classification accuracy of the segmentation results. The experiments are performed on two airborne synthetic aperture radar image databases. It has been proved that our method is superior to other state-of- the-art image segmentation approaches.
Staircase Traversal via Reinforcement Learning for Active Reconfiguration of Assistive Robots

Assistive robots introduce a new paradigm for developing advanced personalized services. At the same time, the variability and stochasticity of environments, hardware and unknown parameters of the interaction complicates their modelling, as in the case of staircase traversal. For this task, we propose to treat the problem of robot configuration control within a reinforcement learning framework, using policy gradient optimization. In particular, we examine the use of safety or traction measures as a means for endowing the learned policy with desired properties. Using the proposed framework, we present extensive qualitative and quantitative results where a simulated robot learns to negotiate staircases of variable size, while being subjected to different levels of sensing noise.
How Much do Hardware Imperfections Affect the Performance of Reconfigurable Intelligent Surface-Assisted Systems?

In the present work, we investigate the impact of transceiver hardware imperfection on reconfigurable intelligent surface (RIS)-assisted wireless systems. In this direction, first, we present a general model that accommodates the impact of the transmitter (TX) and receiver (RX) radio frequency impairments. Next, we derive novel closed-form expressions for the instantaneous end-to-end signal-to-noise-plus-distortion-ratio (SNDR). Building upon these expressions, we extract an exact closed-form expression for the system’s outage probability, which allows us not only to quantify RIS-assisted systems’ outage performance but also reveals that the maximum allowed spectral efficiency of the transmission scheme is limited by the levels of the transceiver hardware imperfection. Likewise, a diversity analysis is provided. Moreover, in order to characterize the capacity of RIS-assisted systems, we report a new upper-bound for the ergodic capacity, which takes into account the number of the RIS’s reflective units (RUs), the level of TX and RX hardware imperfection, as well as the transmission signal-to-noise-ratio (SNR). Finally, two insightful ergodic capacity ceilings are extracted for the high-SNR and high-RUs regimes. Our results highlight the importance of accurately modeling the transceiver hardware imperfection and reveals that they significantly limit the RIS-assisted wireless system performance.
Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning

Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insufficient amount of neurobiological evidence. We investigated the structural and strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize combinations of brain regions, thus representing the most referred-to regions used by the model whilst classifying the images. We also implemented recurrent neural networks to classify the sequence of brain regions efficiently. We found emphatic structural and strategic evidence on which the model heavily relies during the classification process. For instance, we observed that the structural and strategic evidence tends to be associated with subcortical structures, including the basal ganglia (BG). Our work identifies the distinct brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning that clinicians can use to ensure an economical and time-efficient diagnosis process.
Two-Sample Testing on Pairwise Comparison Data and the Role of Modeling Assumptions

A number of applications require two-sample testing of pairwise comparison data. For instance, in crowdsourcing, there is a long-standing question of whether comparison data provided by people is distributed similar to ratings-converted-to-comparisons. Other examples include sports data analysis and peer grading. In this paper, we design a two-sample test for pairwise comparison data. We establish an upper bound on the sample complexity required to correctly distinguish between the distributions of the two sets of samples. Our test requires essentially no assumptions on the distributions. We then prove complementary information-theoretic lower bounds showing that our results are tight (in the minimax sense) up to constant factors. We also investigate the role of modeling assumptions by proving information-theoretic lower bounds for a range of pairwise comparison models (WST, MST, SST, parameter-based such as BTL and Thurstone).
Convolutional Neural Network Based Breast Cancer Histopathology Image Classification

Breast cancer is a global health concern, with approximately 30 million new cases projected to be reported by 2030. While efforts are being channeled into curative measures, preventive and diagnostic measures also need to be improved to curb the situation. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have been widely adopted for the computerized classification of breast cancer histopathology images. In this work, we propose a set of training techniques to improve the performance of CNN-based classifiers for breast cancer identification. We combined transfer learning techniques with data augmentation and whole image training to improve the performance of the CNN classifier. Instead of conventional image patch extraction for training and testing, we employed a high-resolution whole-image training and testing on a modified network that was pre-trained on the Imagenet dataset. Despite the computational complexity, our proposed classifier achieved significant improvement over the previously reported studies on the open-source BreakHis dataset, with an average image level accuracy of about 91% and patient scores as high as 95%. Clinical Relevance— this work improves on the performance of CNN for breast cancer histopathology image classification. An improved Breast cancer image classification can be used for the preliminary examination of tissue slides in breast cancer diagnosis.
Stereo-electroencephalography (SEEG) reference based on low-variance signals

For a correct assessment of stereo-electroencephalographic (SEEG) recordings, a proper signal electrical reference is necessary. Such a reference might be physical or virtual. Physical reference can be noisy and a proper virtual reference calculation is often time-consuming. This paper uses the variance of the SEEG signals to calculate the reference from relatively low noise signals to reduce the contamination by distant sources, while maintaining negligible computing time.Ten patients with SEEG recordings were used in this study. 20-second long recordings from each patient, sampled at 5000 Hz, were used to calculate variances of SEEG signals and a low-variance (LV) subset of signals was selected for each patient. Consequently, 4 different reference signals were calculated using: 1) an average signal from WM contacts only (AVG_WM); 2) an average signal from LV contacts only (AVG_LV); 3) independent component analysis (ICA) method from WM contacts only (ICA_WM); and 4) ICA method from LV signals only (ICA_LV). Also, the original testing reference, an average signal from all SEEG contacts (AVG) was utilized. Finally, bipolar signals and average signals from anatomical structures were calculated and used to evaluate reference signals.91. 7% of the WM SEEG contacts were found below the average variance. ICA_LV showed the best and AVG_WM the worst overall results. AVG_LV had the most positive impact on minimizing the mutual correlations between separate brain structures and correcting the outliers. The average processing time for ICA methods was 66.72 seconds and 0.7870 seconds for AVG methods (100 000 samples, 125.7±20.4 SEEG signals). Utilizing the LV data subset improves the reference signal. WM references are difficult to obtain and seem to be more susceptible to errors caused by low number of WM contacts in the dataset. ICA_LV can be considered as one of the best reference estimations, however the calculation is very demanding and time consuming. AVG_LV shows good and stable results, while it is based on a straightforward methodology and outstandingly fast calculation.