Weixuan Chen

Automatic identification of artifacts in electrodermal activity data

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

An open-source automated platform for three-dimensional visualization of subdural electrodes using CT-MRI coregistration

Visualizing implanted subdural electrodes in three‐dimensional (3D) space can greatly aid in planning, executing, and validating resection in epilepsy surgery. Coregistration software is available, but cost, complexity, insufficient accuracy, or validation limit adoption. We present a fully automated open‐source application, based on a novel method using postimplant computerized tomography (CT) and postimplant magnetic resonance (MR) images, for accurately visualizing intracranial electrodes in 3D space.

Logistic-weighted Regression Improves Decoding of Finger Flexion from Electrocorticographic Signals

One of the most interesting applications of brain computer interfaces (BCIs) is movement prediction. With the development of invasive recording techniques and decoding algorithms in the past ten years, many single neuron-based and electrocorticography (ECoG)-based studies have been able to decode trajectories of limb movements. As the output variables are continuous in these studies, a regression model is commonly used. However, the decoding of limb movements is not a pure regression problem, because the trajectories can be apparently classified into a motion state and a resting state, which result in a binary property overlooked by previous studies. In this paper, we propose an algorithm called logistic-weighted regression to make use of the property, and apply the algorithm to a BCI system decoding flexion of human fingers from ECoG signals. Our results show that the application of logistic-weighted regression improves decoding performance compared to the application of linear regression or pace regression. The proposed algorithm is also immensely valuable in the other BCIs decoding continuous movements.

Wavelet-based motion artifact removal for electrodermal activity.

Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.

Eliminating Physiological Information from Facial Videos

Vital signs, cognitive load, and stress can be remotely measured from human faces using video-capturing devices under ambient light, which raises both wide applications and privacy issues. To avoid immoral use of this technology, there is a need for methods to eliminate physiological information from facial videos without affecting their visual appearance. To meet the need, we develop a novel algorithm based on motion component magnification that inputs a video and outputs its replica with physiological signals removed. Facial video data has been collected from 18 participants in a study to assess the performance of our algorithm in thwarting heart rate measurement based on remote photoplethysmography. Our results show that the mean absolute error of heart rate measurement averaged among participants was increased from 0.254 beats per minute to above 17 beats per minute without causing visible artifact. This is the first demonstration of an algorithm that can achieve this kind of functionality.

Multimodal ambulatory sleep detection

Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.

Organic Primitives: Synthesis and Design of pH-Reactive Materials using Molecular I/O for Sensing, Actuation, and Interaction

In this paper we present Organic Primitives, an enabling toolbox that expands upon the library of input-output devices in HCI and facilitates the design of interactions with organic, fluid-based systems. We formulated color, odor and shape changing material primitives which act as sensor-actuators that convert pH signals into human-readable outputs. Food-grade organic molecules anthocyanin, vanillin, and chitosan were employed as dopants to synthesize materials which output a spectrum of colors, degrees of shape deformation, and switch between odorous and non-odorous states. We evaluated the individual output properties of our sensor-actuators to assess the rate, range, and reversibility of the changes as a function of pH 2-10. We present a design space with techniques for enhancing the functionality of the material primitives, and offer passive and computational methods for controlling the material interfaces. Finally, we explore applications enabled by Organic Primitives under four contexts: environmental, cosmetic, edible, and interspecies.Utilizing a selection of pH-reactive organic compounds, we have synthesized tunable, material-based sensor-actuators that can output a spectrum of colors, degrees of shape deformation, and the switching of odorous to non-odorous based upon pH value; we call these base sensor-actuators Organic Primitives. In this paper, we present the approach of utilizing the pH-reactive organic molecules vanillin, anthocyanin and chitosan as mechanisms for odor, color and shape actuation. These primitive mechanisms can be used to create responsive, functionalized biopolymers across a variety of form factors, including fluids, fibers, films, sheets, gels and dimensional forms. These biopolymers can be used alone or in combination to form simple but highly functionalized systems. The materials are edible and enables fluid-based sensing-actuation near, on, or even within living systems. In this paper, we demonstrate a design space which can enable higher-order functions in the material system. The design space highlights a variety of techniques to control the material system through defining pH inputs, sequencing techniques, encoding hidden 2D and 3D forms, patterning, and compositing functionalized biopolymers. Through this molecular-scale approach of creating tunable sensors through material synthesis, we explore human-material interaction in four application contexts: edible, cosmetic, environmental, and interspecies. In order to evaluate the functional parameters of our material formulations, we evaluate the output properties of individual pH-reactive molecules in the form of films as a function of pH 2 - 10. Though this work only explores pH response, the methods here could potentially be used to create materials responsive to any number of environmental stimuli, expanding upon the library of input-output devices for HCI.

Estimating carotid pulse and breathing rate from near-infrared video of the neck

OBJECTIVE Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. APPROACH This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, principal component analysis for feature extraction, and hidden Markov models for data smoothing. MAIN RESULTS We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. SIGNIFICANCE This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.

Predicting Perceived Emotions in Animated GIFs with 3D Convolutional Neural Networks

Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored before. To help with the search and recommendation of GIFs, we aim to predict their emotions perceived by humans based on their contents. Since previous solutions to this problem only utilize image-based features and lose all the motion information, we propose to use 3D convolutional neural networks (CNNs) to extract spatiotemporal features from GIFs. We evaluate our methodology on a crowd-sourcing platform called GIFGIF with more than 6000 animated GIFs, and achieve a better accuracy then any previous approach in predicting crowd-sourced intensity scores of 17 emotions. It is also found that our trained model can be used to distinguish and cluster emotions in terms of valence and risk perception.

Individualized cortical function mapping using high gamma activity

How to reduce the risk of damaging the epilepsy patients vital function areas during resection of epileptic focus remains a challenge for neurosurgeon. Clinically used electrical cortical stimulation (ECS) method shows limits on accuracy, efficiency and reliability. In this study, a cortical function mapping method with 3D visualization was implemented by analyzing and projecting the power change of high gamma (HG) oscillation in ECoG on patients own MRI brain model. The method was tested on epilepsy patients with subdural electrodes for three tasks (hand movement, tongue movement and silent reading). The proposed 3D cortical function mapping on the patients individual brain structure provides direct and accurate reference for resection surgery planning.