摘要 :
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, a larger BOD group has been assembled, inclu...
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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, 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(N~3) for N=10~(2～3) 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 Spacefilling 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 ≡ 0(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 e.g. at V1-
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摘要 :
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, a larger BOD group has been assembled, inclu...
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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, 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(N~3) for N=10~(2～3) 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 Spacefilling 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 ≡ 0(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 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℃; Noise=27℃: 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.
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Propo...
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Proportional Integral Derivative (PID) controllers, are a popular and effective way to control these temperature systems. In this paper we explore different types of control for a prototype heated reference target. We show that we can use a combination of PID and Least Means Square (LMS) closed loop adaptive control to determine both the optimal weight proportion and the magnitude of the weights for optimal power draw. This enables us to develop a faster, more optimal controller than by manually tuning the weights by hand.
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Propo...
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In the field of IR technology, careful regulation of temperature elements such as blackbodies or temperature targets is important, particularly for calibration. Feedback based controllers, independent of state space, such as Proportional Integral Derivative (PID) controllers, are a popular and effective way to control these temperature systems. In this paper we explore different types of control for a prototype heated reference target. We show that we can use a combination of PID and Least Means Square (LMS) closed loop adaptive control to determine both the optimal weight proportion and the magnitude of the weights for optimal power draw. This enables us to develop a faster, more optimal controller than by manually tuning the weights by hand.
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In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity (EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras ca...
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In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity (EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras can passively and remotely obtain an analog to EDA by assessing the activation of facial eccrine skin pores. This paper investigates a method to distinguish facial skin from non-skin portions on the face to generate a skin-only Dynamic Mask (DM), validates the DM results, and demonstrates DM performance by removing false pore counts. Moreover, this paper shows results from these techniques using data from 20+ subjects across two different experiments. In the first experiment, subjects were presented with primary screening questions for which some had jeopardy. In the second experiment, subjects experienced standard emotion-eliciting stimuli. The results from using this technique will be shown in relation to data and human perception (ground truth). This paper introduces an automatic end-to-end skin detection approach based on texture feature vectors. In doing so, the paper contributes not only a new capability of tracking facial skin in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for determining autonomic nervous system responses for medical and security applications.
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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...
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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.
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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...
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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.
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Measuring the performance of a cathode ray tube (CRT) or liquid crystal display (LCD) is necessary to enable end-to-end system modeling and characterization of currently used high performance analog imaging systems, such as 2nd Ge...
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Measuring the performance of a cathode ray tube (CRT) or liquid crystal display (LCD) is necessary to enable end-to-end system modeling and characterization of currently used high performance analog imaging systems, such as 2nd Generation FLIR systems. If the display is color, the performance measurements are made more difficult because of the underlying structure of the color pixel as compared to a monochrome pixel. Out of the various characteristics of interest, we focus on determining the gamma value of a display. Gamma quantifies the non-linear response between the input gray scale and the displayed luminance. If the displayed image can be corrected for the display's gamma, an accurate scene can be presented or characterized for laboratory measurements such as MRT (Minimum Resolvable Temperature) and CTF (Contrast Threshold Function). In this paper, we present a method to determine the gamma to characterize a color display using the Prichard 1980A photometer. Gamma corrections were applied to the test images for validating the accuracy of the computed gamma value. The method presented here is a simple one easily implemented employing a Prichard photometer.
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Measuring the performance of a cathode ray tube (CRT) or liquid crystal display (LCD) is necessary to enable end-to-end system modeling and characterization of currently used high performance analog imaging systems, such as 2nd Ge...
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Measuring the performance of a cathode ray tube (CRT) or liquid crystal display (LCD) is necessary to enable end-to-end system modeling and characterization of currently used high performance analog imaging systems, such as 2nd Generation FLIR systems. If the display is color, the performance measurements are made more difficult because of the underlying structure of the color pixel as compared to a monochrome pixel. Out of the various characteristics of interest, we focus on determining the gamma value of a display. Gamma quantifies the non-linear response between the input gray scale and the displayed luminance. If the displayed image can be corrected for the display's gamma, an accurate scene can be presented or characterized for laboratory measurements such as MRT (Minimum Resolvable Temperature) and CTF (Contrast Threshold Function). In this paper, we present a method to determine the gamma to characterize a color display using the Prichard 1980A photometer. Gamma corrections were applied to the test images for validating the accuracy of the computed gamma value. The method presented here is a simple one easily implemented employing a Prichard photometer.
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Measuring the Modulation Transfer Function (MTF) of a display monitor is necessary for many applications such as: modeling end-to-end systems, conducting perception experiments, and performing targeting tasks in real-word scenario...
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Measuring the Modulation Transfer Function (MTF) of a display monitor is necessary for many applications such as: modeling end-to-end systems, conducting perception experiments, and performing targeting tasks in real-word scenarios. The MTF of a display defines the resolution properties and quantifies how well the spatial frequencies are displayed on a monitor. Many researchers have developed methods to measure display MTFs using either scanning or imaging devices. In this paper, we first present methods to measure display MTFs using two separate technologies and then discuss the impact of a display MTF on a system's performance. The two measurement technologies were scanning with a photometer and imaging with a CMOS based camera. To estimate a true display MTF, measurements made with the photometer were backed out for the scanning optics aperture. The developed methods were applied to measure MTFs of the two types of monitors, Cathode Ray Tube (CRT) and Liquid Crystal Display (LCD). The accuracy of the measured MTFs was validated by comparing MTFs measured with the two systems. The methods presented here are simple and can be easily implemented employing either a Prichard photometer or an imaging device. In addition, the impact of a display MTF on the end-to-end performance of a system was modeled using NV-IPM.
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