Balvinder Kaur

Brain order disorder 2nd group report of f-EEG

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain – the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human’s effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E − TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen’s novel “Thinking fast and slow”, through the brainwave biofeedback we can first identify an individual’s “anchored cognitive bias sources”. This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~’) of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, “Peano-Hilbert curve,” SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble <IRF< e.g. at V1-V4 of Brodmann areas 17-19 of the Cortex, i.e. stationary Wiener-Kintchine-Einstein Theorem. Goal#1: functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C; Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine care-giver networks. Aging global villagers need mental healthcare devices that are affordable, harmless, administrable (AHA) and user-friendly, situated in a clothing article such as a baseball hat and able to interface with pervasive Smartphones in daily environment.

Heart rate variability (HRV): an indicator of stress

Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy [3], [4], [10] & [11]. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject’s normal mental state from a stressed one [4], [13] & [14]. In all of these past works, although processing is done in both frequency and time domains, few classification algorithms have been explored for classifying normal from stressed RRintervals. In this paper we used 30 s intervals from the Electrocardiogram (ECG) time series collected during normal and stressed conditions, produced by means of a modified version of the Trier social stress test, to compute HRV-driven features and subsequently applied a set of classification algorithms to distinguish stressed from normal conditions. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications, namely 1) logistic regression (LR) [16] and 2) linear discriminant analysis (LDA) [6]. Classification performance for various levels of stress over the entire test was quantified using precision, accuracy, sensitivity and specificity measures. Results from both classifiers were then compared to find an optimal classifier and HRV features for stress detection. This work, performed under an IRB-approved protocol, not only provides a method for developing models and classifiers based on human data, but also provides a foundation for a stress indicator tool based on HRV. Further, these classification tools will not only benefit many civilian applications for detecting stress, but also security and military applications for screening such as: border patrol, stress detection for deception [3],[17], and wounded-warrior triage [12].

Digital imaging and remote sensing image generator (DIRSIG) as applied to NVESD sensor performance modeling

The US Army’s Communications Electronics Research, Development and Engineering Center (CERDEC) Night Vision and Electronic Sensors Directorate (referred to as NVESD) is developing a virtual detection, recognition, and identification (DRI) testing methodology using simulated imagery as a means of augmenting the field testing component of sensor performance evaluation, which is expensive, resource intensive, time consuming, and limited to the available target(s) and existing atmospheric visibility and environmental conditions at the time of testing. Existing simulation capabilities such as the Digital Imaging Remote Sensing Image Generator (DIRSIG) and NVESD’s Integrated Performance Model Image Generator (NVIPM-IG) can be combined with existing detection algorithms to reduce cost/time, minimize testing risk, and allow virtual/simulated testing using full spectral and thermal object signatures, as well as those collected in the field. NVESD has developed an end-to-end capability to demonstrate the feasibility of this approach. Simple detection algorithms have been used on the degraded images generated by NVIPM-IG to determine the relative performance of the algorithms on both DIRSIG-simulated and collected images. Evaluating the degree to which the algorithm performance agrees between simulated versus field collected imagery is the first step in validating the simulated imagery procedure.

Remote stress detection using a visible spectrum camera

Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject’s normal mental state from a stressed one. In all of these past works, HRV analysis is performed on the cardiac activity data acquired by conventional electrocardiography electrodes, which may introduce additional stress and complexity to the acquired data. In this paper we use remotely acquired time-series data extracted from the human facial skin reflectivity signal during rest and mental stress conditions to compute HRV driven features. We further apply a set of classification algorithms to distinguishing between these two states. To determine heart beat signal from the facial skin reflectivity, we apply Principal Component Analysis (PCA) for denoising and Independent Component Analysis (ICA) for source selection. To determine the signal peaks to extract the RR-interval time-series, we apply a threshold-based detection technique and additional peak conditioning algorithms. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications such as logistic regression and linear discriminant analysis (LDA). Goodness of each classifier is measured in terms of sensitivity/specificity. Results from each classifier are then compared to find the optimal classifier for stress detection. This work, performed under an IRB approved protocol, provides initial proof that remotely-acquired heart rate signal can be used for stress detection. This result shows promise for further development of a remote-sensing stress detection technique both for medical and deception-detection applications.

Remotely detected differential pulse transit time as a stress indicator

The human cardiovascular system, controlled by the autonomic nervous system (ANS), is one of the first sites where one can see the “fight-or-flight” response due to the presence of external stressors. In this paper, we investigate the possibility of detecting mental stress using a novel measure that can be measured in a contactless manner: Pulse transit time (dPTT), which refers to the time that is required for the blood wave (BW) to cover the distance from the heart to a defined remote location in the body. Loosely related to blood pressure, PTT is a measure of blood velocity, and is also implicated in the “fight-or-flight” response. We define the differential PTT (dPTT) as the difference in PTT between two remote areas of the body, such as the forehead and the palm. Expanding our previous work on remote BW detection from visible spectrum videos, we built a system that remotely measures dPTT. Human subject data were collected under an IRB approved protocol from 15 subjects both under normal and stress states and are used to initially establish the potential use of remote dPPT detection as a stress indicator.

Hyperspectral waveband group optimization for time-resolved human sensing

Pulse and respiration rates provide vital information for evaluating the physiological state of an individual during triage. Traditionally, pulse and respiration have been tracked by means of contact sensors. Recent work has shown that visible cameras can passively and remotely obtain pulse signals under controlled environmental conditions [2] [5] [14] [27]. This paper introduces methods for extracting and characterizing pulse and respiration signals from skin reflectivity data captured in peak sensitivity range for silicon detector (400nm-1100nm). Based on the physiological understanding [12] [13] [15] of human skin and reflectivity at various skin depths, we optimize a group of spectral bands to determine pulse and respiration with high Peak Signal-to-Noise Ratio (PSNR) and correlation values [27] [30]. Our preliminary results indicate top six optimal waveband groups in about 100nm - 200nm resolution in each, with rank-ordered peaks at 409nm, 512nm, 584nm, 667nm, 885nm and 772nm. This work, collected under an approved IRB protocol enhances non-contact, remote, passive, and real-time measurement of pulse and respiration for security and medical applications.

Adaptive Region of Interest (ROI) detection and tracking for respiration measurement in thermal video

Respiration rate is a key guide for evaluating the physiological state of an individual during triage. Recent work has shown that high resolution thermal cameras can passively and remotely obtain respiration signals under controlled environmental conditions. This paper introduces an automatic end-to-end respiration signal measurement (through signal detection) approach based on statistical computation of the image intensities around the human nostril area in a thermal video. A method is presented to detect and track the nostril area and to calculate statistical values of the pixel intensity around the nostril area and correlate the statistical values with respiration signals from a contact sensor such as transducer belt. Results are based upon data collected from 200 subjects across two different experiments. This work provides not only a new image processing tool for tracking facial ROIs in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for measuring respiration for security and medical applications.

The use of spectral skin reflectivity and laser doppler vibrometry data to determine the optimal site and wavelength to collect human vital sign signatures

The carotid artery has been used extensively by researchers to demonstrate that Laser Doppler Vibrometry (LDV) is capable of exploiting vital sign signatures from cooperative human subjects at stando. Research indicates that, the carotid, although good for cooperative and non-traumatic scenarios, is one of the first vital signs to become absent or irregular when a casualty is hemorrhaging and in progress to circulatory (hypovolemic) shock. In an effort to determine the optimal site and wavelength to measure vital signs off human skin, a human subject data collection was executed whereby 14 subjects had their spectral skin reflectivity and vital signs measured at five collection sites (carotid artery, chest, back, right wrist and left wrist). In this paper, we present our findings on using LDV and re ectivity data to determine the optimal collection site and wavelength that should be used to sense pulse signals from quiet and relatively motionless human subjects at stando. In particular, we correlate maximum levels of re ectivity across the ensemble of 14 subjects with vital sign measurements made with an LDV at two ranges, for two scenarios.

Motion correction for improved estimation of heart rate using a visual spectrum camera

Heart rate measurement using a visual spectrum recording of the face has drawn interest over the last few years as a technology that can have various health and security applications. In our previous work, we have shown that it is possible to estimate the heart beat timing accurately enough to perform heart rate variability analysis for contactless stress detection. However, a major confounding factor in this approach is the presence of movement, which can interfere with the measurements. To mitigate the effects of movement, in this work we propose the use of face detection and tracking based on the Karhunen-Loewe algorithm in order to counteract measurement errors introduced by normal subject motion, as expected during a common seated conversation setting. We analyze the requirements on image acquisition for the algorithm to work, and its performance under different ranges of motion, changes of distance to the camera, as well and the effect of illumination changes due to different positioning with respect to light sources on the acquired signal. Our results suggest that the effect of face tracking on visual-spectrum based cardiac signal estimation depends on the amplitude of the motion. While for larger-scale conversation-induced motion it can significantly improve estimation accuracy, with smaller-scale movements, such as the ones caused by breathing or talking without major movement errors in facial tracking may interfere with signal estimation. Overall, employing facial tracking is a crucial step in adapting this technology to real-life situations with satisfactory results.

Visible spectrum-based non-contact HRV and dPTT for stress detection

Stress is a major health concern that not only compromises our quality of life, but also affects our physical health and well-being. Despite its importance, our ability to objectively detect and quantify it in a real-time, non-invasive manner is very limited. This capability would have a wide variety of medical, military, and security applications. We have developed a pipeline of image and signal processing algorithms to make such a system practical, which includes remote cardiac pulse detection based on visible spectrum videos and physiological stress detection based on the variability in the remotely detected cardiac signals. First, to determine a reliable cardiac pulse, principal component analysis (PCA) was applied for noise reduction and independent component analysis (ICA) was applied for source selection. To determine accurate cardiac timing for heart rate variability (HRV) analysis, a blind source separation method based least squares (LS) estimate was used to determine signal peaks that were closely related to R-peaks of the electrocardiogram (ECG) signal. A new metric, differential pulse transit time (dPTT), defined as the difference in arrival time of the remotely acquired cardiac signal at two separate distal locations, was derived. It was demonstrated that the remotely acquired metrics, HRV and dPTT, have potential for remote stress detection. The developed algorithms were tested against human subject data collected under two physiological conditions using the modified Trier Social Stress Test (TSST) and the Affective Stress Response Test (ASRT). This research provides evidence that the variability in remotely-acquired blood wave (BW) signals can be used for stress (high and mild) detection, and as a guide for further development of a real-time remote stress detection system based on remote HRV and dPTT.