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Results for “Cognitive State Monitoring”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Effects of Varying Gravity Levels on fNIRS Headgear Performance and Signal Recovery

This paper reviews the effects of varying gravitational levels on functional Near-Infrared Spectroscopy (fNIRS) headgear. The fNIRS systems quantify neural activations in the cortex by measuring hemoglobin concentration changes via optical intensity. Such activation measurement allows for the detection of cognitive state, which can be important for emotional stability, human performance and vigilance optimization, and the detection of hazardous operator state. The technique depends on coupling between the fNIRS probe and users skin. Such coupling may be highly susceptible to motion if probe-containing headgear designs are not adequately tested. The lack of reliable and self-applicable headgear robust to the influence of motion artifact currently inhibits its operational use in aerospace environments. Both NASAs Aviation Safety and Human Research Programs are interested in this technology as a method of monitoring cognitive state of pilots and crew.

fNIRS↗
NASA NTRS2017

Waveguide Modulator for Interference Tolerant Functional Near Infrared Spectrometer (fNIRS)

Many crew-related errors in aviation and astronautics are caused by hazardous cognitive states including overstress, disengagement, high fatigue and ineffective crew coordination. Safety can be improved by monitoring and predicting these cognitive states in a non-intrusive manner and designing mitigation strategies. Measuring hemoglobin concentration changes in the brain with functional Near Infrared Spectroscopy is a promising technique for monitoring cognitive state and optimizing human performance during both space and aviation operations. A compact, wearable fNIRS system would provide an innovative early warning system during long duration missions to detect and prevent vigilance decrements in pilots and astronauts. This effort focused on developing a waveguide modulator for use in a fNIRS system.

spectroscopy↗
NASA NTRS2013

Toward Adaptation of fNIRS Instrumentation to Airborne Environments

The paper reviews potential applications of functional Near-Infrared Spectroscopy (fNIRS), a well-known medical diagnostic technique, to monitoring the cognitive state of pilots with a focus on identifying ways to adopt this technique to airborne environments. We also discuss various fNIRS techniques and the direction of technology maturation of associated hardware in view of their potential for miniaturization, maximization of data collection capabilities, and user friendliness.

aircraft safety↗
NASA NTRS2014

Toward Adaptation of fNIRS Instrumentation to Airborne Environments

The paper reviews potential applications of functional Near-Infrared Spectroscopy (fNIRS), a well-known medical diagnostic technique, to monitoring the cognitive state of pilots with a focus on identifying ways to adopt this technique to airborne environments. We also discuss various fNIRS techniques and the direction of technology maturation of associated hardware in view of their potential for miniaturization, maximization of data collection capabilities, and user friendliness.

aircraft instrumentation↗
NASA NTRS1992

Electroencephalographic monitoring of complex mental tasks

Outlined here is the development of neurophysiological procedures to monitor operators during the performance of cognitive tasks. Our approach included the use of electroencepalographic (EEG) and rheoencephalographic (REG) techniques to determine changes in cortical function associated with cognition in the operator's state. A two channel tetrapolar REG, a single channel forearm impedance plethysmograph, a Lead I electrocardiogram (ECG) and a 21 channel EEG were used to measure subject responses to various visual-motor cognitive tasks. Testing, analytical, and display procedures for EEG and REG monitoring were developed that extend the state of the art and provide a valuable tool for the study of cerebral circulatory and neural activity during cognition.

Guisado, Raul↗
NASA NTRSDate not supplied

Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty

The development of a synergetic system between humans and technology is a challenge that the scientific community has been facing for many years. Our aeronautic research aims to enhance this synergy between humans and machines through predictive human performance modeling for systems to mitigate high-risk situations. By being able to predict and anticipate human states, the crew monitoring system should be able to adjust and support the pilot for aviation safety. Our work focusing on attention-related human performance-limiting states (AHPLS) that impact a pilot’s performance and introduce high-risk catastrophic situations [1]. For example, AHPLS has been cited as a causal factor in more than 50% of all loss control in flight and thus contributes significantly toward commercial aviation fatalities [1, 2]. Cognitive state and its physiological fingerprint can be valuable information for this detecting AHPLS, but human cognitive state detection is still a major limitation for these crew monitoring systems.

machine learning↗
NASA NTRS2020

Frequency Domain Functional Near-Infrared Spectrometer (fNIRS) for Crew State Monitoring

A frequency domain functional near-infrared spectrometer (fNIRS) and accompanying software have been developed by the NASA Glenn Research Center as part of the Airspace Operations and Safety Program (AOSP) Technologies for Airplane State Awareness (TASA)—SE211 Crew State Monitoring (CSM) Project. The goal of CSM was to develop a suite of instruments to measure the cognitive state of operators while performing operational activities. The fNIRS was one of the instruments intended for the CSM, developed to measure changes in oxygen levels in the brain noninvasively.

Jeffrey R Mackey↗
NASA NTRS2019

Psychophysiological Monitoring of Aerospace Crew State

As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.

Wusk, Grace C.↗
NASA NTRS2012

Best Practices for the Application of Functional Near Infrared Spectroscopy to Operator State Sensing

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuronal measurement technique with many advantages for application in operational and training contexts. Instrumentation and protocol improvements, however, are required to obtain useful signals and produce expeditiously self-applicable, comfortable and unobtrusive headgear. Approaches for improving the validity and reliability of fNIRS data for the purpose of sensing the mental state of commercial aircraft operators are identified, and an exemplary system design for attentional state monitoring is outlined. Intelligent flight decks of the future can be responsive to state changes to optimally support human performance. Thus, the identification of cognitive performance decrement, such as lapses in operator attention, may be used to predict and avoid error-prone states. We propose that attentional performance may be monitored with fNIRS through the quantification of hemodynamic activations in cortical regions which are part of functionally-connected attention and resting state networks. Activations in these regions have been shown to correlate with behavioral performance and task engagement. These regions lie beneath superficial tissue in head regions beyond the forehead. Headgear development is key to reliably and robustly accessing locations beyond the hair line to measure functionally-connected networks across the whole head. Human subject trials using both fNIRS and functional Magnetic Resonance Imaging (fMRI) will be used to test this system. Data processing employs Support Vector Machines for state classification based on the fNIRS signals. If accurate state classification is achieved based on sensed activation patterns, fNIRS will be shown to be useful for monitoring attentional performance.

Harrivel, Angela R.↗
NASA NTRS1995

Rheoencephalographic and electroencephalographic measures of cognitive workload: analytical procedures

This investigation demonstrates the feasibility of mental workload assessment by rheoencephalographic (REG) and multichannel electroencephalographic (EEG) monitoring. During the performance of this research, unique testing, analytical and display procedures were developed for REG and EEG monitoring that extend the current state of the art and provide valuable tools for the study of cerebral circulatory and neural activity during cognition. REG records are analyzed to provide indices of the right and left hemisphere hemodynamic changes that take place during each test sequence. The EEG data are modeled using regression techniques and mathematically transformed to provide energy-density distributions of the scalp electrostatic field. These procedures permit concurrent REG/EEG cognitive testing not possible with current techniques. The introduction of a system for recording and analysis of cognitive REG/EEG test sequences facilitates the study of learning and memory disorders, dementia and other encephalopathies.

Arousal/physiology↗
NASA NTRSDate not supplied

Astrobee Operations on the Iss: Gui’S Impact on the Operators’ Cognitive Load

The Astrobee free-flying robots have completed their fifth successful year of operations housed in the Japanese Experimental Module (JEM) on the International Space Station (ISS). In this paper, we introduce two Graphical User Interfaces (GUI) used to operate and monitor in real-time the Astrobee free-flying robots onboard the ISS and report on the impact these GUIs have in the operator’s cognitive load. A review of the state-of-the-art GUI design for remotely teleoperated scenarios with minimal time delay is presented and the study’s conclusion used to determine the elements and recommendations to create an interface that minimizes its impact on the overall performance of an operator during an activity at the ISS. The Ground Data System (GDS) is one of the two GUIs in the study: it contains several tabs, each of which displays a different set of controls for specific tasks e.g. Overview, Run Plan, Teleoperate, Guest Science; some also display video and a three-dimensional (3D) representation of the ISS and robot based on the Astrobee’s telemetry. Most tabs enable a single operator-robot connection, however some of its tabs are capable to monitor and control up to three Astrobees simultaneously. The GDS Helper is a text-based user interface created to facilitate commanding and monitoring of an Astrobee robot directly from an SSH session. In full interactive mode it displays a maximum of 5 sections: general commanding, feedback/ack, telemetry, guest science commanding, and data, all in one view. In batch mode, it enables complex command scripting while retaining some interactive capabilities. A comparative analysis between these GUIs is carried out at an analogous ISS environment at the NASA Ames Research Center’s Granite Lab and its results presented. While GDS is able to provide an operator with control and situational awareness via its video and 3D displays, its several tabs may introduce an overwhelming amount of information confusing and delaying the operator especially during time-sensitive maneuvers where the operator may need to switch back and forth between them. GDS helper in the other hand does not provide video or 3D displays thus not allowing an operator to attain situational awareness, however it provides the operator with a design displaying commonly used data in a single window, enabling the operator to understand the state of the robot at a glance and control it through a commands entered via keyboard instead of a combination of mouse clicks and keyboard input. The results of the experiments measure the cognitive load across several operators maneuvering Astrobee to accomplish tasks ranging from fully manual to supervised activities. A GUI combining a single window displaying data along video and a 3D display is expected to reduce the operator’s cognitive load.

Astrobee↗
NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗
NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗
NASA NTRS2005

Recreation Embedded State Tuning for Optimal Readiness and Effectiveness (RESTORE)

Physiological self-regulation training is a behavioral medicine intervention that has demonstrated capability to improve psychophysiological coping responses to stressful experiences and to foster optimal behavioral and cognitive performance. Once developed, these psychophysiological skills require regular practice for maintenance. A concomitant benefit of these physiologically monitored practice sessions is the opportunity to track crew psychophysiological responses to the challenges of the practice task in order to detect shifts in adaptability that may foretell performance degradation. Long-duration missions will include crew recreation periods that will afford physiological self-regulation training opportunities. However, to promote adherence to the regimen, the practice experience that occupies their recreation time must be perceived by the crew as engaging and entertaining throughout repeated reinforcement sessions on long-duration missions. NASA biocybernetic technologies and publications have developed a closed-loop concept that involves adjusting or modulating (cybernetic, for governing) a person's task environment based upon a comparison of that person's physiological responses (bio-) with a training or performance criterion. This approach affords the opportunity to deliver physiological self-regulation training in an entertaining and motivating fashion and can also be employed to create a conditioned association between effective performance state and task execution behaviors, while enabling tracking of individuals psychophysiological status over time in the context of an interactive task challenge. This paper describes the aerospace spin-off technologies in this training application area as well as the current spin-back application of the technologies to long-duration missions - the Recreation Embedded State Tuning for Optimal Readiness and Effectiveness (RESTORE) concept. The RESTORE technology is designed to provide a physiological self-regulation training countermeasure for maintaining and reinforcing cognitive readiness, resilience under psychological stress, and effective mood states in long-duration crews. The technology consists of a system for delivering physiological self-regulation training and for tracking crew central and autonomic nervous system function; the system interface is designed to be experienced as engaging and entertaining throughout repeated training sessions on long-duration missions. Consequently, this self-management technology has threefold capability for recreation, behavioral health problem prophylaxis and remediation, and psychophysiological assay. The RESTORE concept aims to reduce the risk of future manned exploration missions by enhancing the capability of individual crewmembers to self-regulate cognitive states through recreation-embedded training protocols to effectively deal with the psychological toll of long-duration space flight.

Pope, Alan T.↗
NASA NTRS1988

Processing deficits in monitoring analog and digital displays: Implications for attentional theory and mental-state estimation research

Subjects performed short term memory tasks, involving both spatial and verbal components, and a visual monitoring task involving either analog or digital display formats. These two tasks (memory vs. monitoring) were performed both singly and in conjunction. Contrary to expectations derived from multiple resource theories of attentional processes, there was no evidence that when the two tasks involved the same cognitive codes (i.e., either both spatial or both verbal/linguistics) there was more of a dual task performance decrement than when the two tasks employed different cognitive codes/processes. These results are discussed in terms of their implications for theories of attentional processes and also for research in mental state estimation.

Payne, David G.↗