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Electroencephalography (EEG) has been widely used in human brain research. Several techniques in EEG relies on analyzing the topographical distribution of the data. One of the most common analysis is EEG microstate (EEG-ms). EEG-ms reflects the stable topographical representation of EEG signal lasting a few dozen milliseconds. EEG-ms were associated with resting state fMRI networks and were associated with mental processes and abnormalities.

Smartphone video-based measurement of heart rate typically uses photoplethysmography (PPG). Prior accuracy studies report low mean absolute errors for apps based on contact PPG on a fingertip, but substantial errors on a troubling percentage of measurements. In this study, we aimed to reduce the rate of substantial heart rate estimation errors by introducing a novel signal present in fingertip videos: fingertip contact surface area.

Speech production involves the synchronization of neural activity between the speech centers of the brain and the oralmotor system, allowing for the conversion of thoughts into

To achieve real-time electrocardiography (ECG) telemonitoring, we need to overcome the scarce bandwidth. Compressed sensing (CS) emerges as a promising technique to greatly compress ECG signal with little computation. Furthermore, with edge-classification, we can reduce the data rate by transmitting abnormal ECG signals only. However, there are three main limitations: limited number of labeled ECG signal, tight battery constraint of edge devices and low response time requirement.

Canonical correlation analysis (CCA) is a data-driven method that has been successfully used in functional magnetic resonance imaging (fMRI) data analysis. Standard CCA extracts meaningful information from a data set by seeking pairs of linear combinations from two sets of variables with maximum pairwise correlation. So far, however, this method has been used without incorporating prior information available for fMRI data. In this paper, we address this issue by proposing a new CCA method named PCCA (for projection CCA).

Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (biologic) sample can be coded before the FTI acquisition in a procedure called Coded Illumination-FTI (CI-FTI). This turns HS data reconstruction into a Compressive Sensing (CS) problem regularized by the sparsity of the HS data. CI-FTI combines the high spectral resolution of FTI with the advantages of reduced-light-exposure imaging in biology.

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method.