
- Read more about RTip : A Fully Automated Root Tip Tracker for Measuring Plant Growth With Intermittent Perturbations
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RTip is a tool to quantify plant root growth velocity using high-resolution microscopy image sequences at sub-pixel accuracy. The fully automated RTip tracker is designed for high-throughput analysis of plant phenotyping experiments with episodic perturbations. RTip is able to auto-skip past these manual intervention perturbation activity, i.e. when the root tip is not under the microscope, the image is distorted or blurred. RTip provides the most accurate root growth velocity results with the lowest variance (i.e.
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- Read more about HIGH-ACCURACY CLASSIFICATION OF ATTENTION DEFICIT HYPERACTIVITY DISORDER WITH L2,1-NORM LINEAR DISCRIMINANT ANALYSIS
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- Read more about FORMULATING DIVERGENCE FRAMEWORK FOR MULTICLASS MOTOR IMAGERY EEG BRAIN COMPUTER INTERFACE
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The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonalization (JAD) to optimize stationarity for multiclass motor imagery Brain Computer Interface (BCI) in an information theoretic framework. Specifically, in the proposed method, we estimate the subspace which optimizes the discriminability between the classes and simultaneously preserve stationarity within the motor imagery classes.
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- Read more about An LSTM Based Architecture to Relate Speech Stimulus to EEG
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icassp2020.pdf

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- Read more about Automatic Classification of Volumes of Water using Swallow Sounds from Cervical Auscultation
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The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can characterize volumes of swallow using CA, they require manual annotations of the different components in the sound.
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Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they performed a cognitively demanding task over extended periods of time. We employed a set of biomarkers to identify mental fatigue and a gradient boosting classifier to predict the performance outcome, seconds prior to the actual behavior response.
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- Read more about ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION
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poster.pdf

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- Read more about Cross-domain Joint Dictionary Learning for ECG Reconstruction from PPG
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An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventive healthcare. Previous reconstruction using a universal basis has limited accuracy due to the lack of rich representative power. This paper proposes a cross-domain joint dictionary learning (XDJDL) framework to maximize the expressive power for the two cross-domain signals.
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- Read more about Online Graph Topology Inference with Kernels for Brain Connectivity Estimation - ICASSP 2020 slides
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- Read more about Blood Pressure Estimation from PPG Signals Using Convolutional Neural Networks and Siamese Network
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Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasive cuff-less BP estimation using photoplethysmography (PPG) signals with Convolutional Neural Networks (CNNs). The first technique is calibration-free.
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