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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 NTRS1976

Automated electroencephalography system and electroencephalographic coordinates of space motion sickness, part 1

A self-contained and portable device which permits clinical electroencephalography (EEG) to be conducted in remote locations by minimally trained, nontechnical personnel was developed and tested. The unit accomplishes semiautomatic acquisition of EEG data from the patient, simultaneous transmission of eight data channels to a central hospital facility over conventional telephone equipment, and automatic printing (at the remote site) of the EEG report generated at the central location. Consequently, this system enables the delivery of high-quality EEG diagnostic services in a geographically remote site with the accuracy and speed formerly possible only in certain large medical centers. Beside obvious potential clinical applications, this system serves as an initial prototype of a unit which could provide inflight EEG during future space missions.

Frost, J. D., Jr.↗
NASA NTRS1976

Automated electroencephalography system and electroencephalographic correlates of space motion sickness, part 2

Sleep pattern alterations were detected in two subjects by electroencephalographic, electrographic, and electromyographic monitoring before, during and after a 28 day bed rest. Standardized criteria were used for data analysis. During the second half of the bed-rest period, sleep latency and stage 3 increased, while total sleep time, stage 2, and REM latency decreased. In addition, during bed rest both subjects showed an increase in the number of REM periods and a slight increase in stage REM amount. No major alterations were seen in the recovery period. Of the alterations found to be associated with bed rest, only one, the increase in stage 3 sleep, was also seen consistently during Skylab. Conversely, none of the postflight changes seen following Skylab were observed during the post-bed-rest recovery period.

Frost, J. D., Jr.↗
NASA NTRS1977

Automated electroencephalography system and electroencephalographic correlates of space motion sickness, part 3

Computer quantification methods were used to analyze the Skylab electroencephalographic data obtained during the course of the M133 series of experiments. This undertaking was prompted by initial observations made during visual analysis of the tape-recorded sleep records where there appeared to be an increase of the alpha-rhythm frequency during some inflight recording sessions, as compared to preflight baseline observations. A number of potential etiological factors are identified and their various possible influences discussed. The presence of the zero-g state is thought to be an important factor, possibly influencing EEG through alteration of vestibular function and/or by producing fluid shifts secondary to loss of hydrostatic pressure.

Frost, J. D., Jr.↗
NASA NTRS1977

Automated electroencephalography system and electroencephalographic correlates of space motion sickness, part 4

Comparative data for further assessments of the EEG alterations seen during Skylab are elaborated. The variability of alpha, beta, theta, and delta EEG characteristics was analyzed with quantitative computer techniques in a group of six normal individuals over a period of two months, and the EEG effects of a prolonged period of bed rest were evaluated in two subjects. The results confirm that the inflight EEG changes seen during Skylab are statistically significant, but the absolute values obtained for the various parameters do not exceed the maximal range expected in a normal population. Further, the EEG manifestations of extended bed rest do not appear similar to those of space flight.

Frost, J. D., Jr.↗
NASA NTRS1986

Nocturnal sleep and daytime alertness of aircrew after transmeridian flights

The nocturnal sleep and daytime alertness of aircrew were studied by electroencephalography and the multiple sleep latency test. After a transmeridian flight from London To San Francisco, sleep onset was faster and, although there was increased wakefulness during the second half of the night, sleep duration and efficiency over the whole night were not changed. The progressive decrease in sleep latencies observed normally in the multiple sleep latency test during the morning continued throughout the day after arrival. Of the 13 subjects, 12 took a nap of around 1-h duration in the afternoon preceding the return flight. These naps would have been encouraged by the drowsiness at this time and facilitated by the departure of the aircraft being scheduled during the early evening. An early evening departure had the further advantage that the circadian increase in vigilance expected during the early part of the day would occur during the latter part of the return flight.

Nicholson, Anthony N.↗
NASA NTRS1988

Physiological measures and mental-state assessment

General considerations regarding monitoring of operators for alertness are discussed, including who should be monitored and what information should be collected. Measures that have been used to ascertain more general and persistent states of alertness are outlined, including cardiac activity, peripheral vascular activity, skin conductance, electroencephalography, pupillography, oculomotor activity, and body movements.

Stern, John A.↗
NASA NTRS1994

Video Game Adapts To Brain Waves

Electronic training system based on video game developed to help children afflicted with attention-deficit disorder (ADD) learn to prolong their attention spans. Uses combination of electroencephalography (EEG) and adaptive control to encourage attentiveness. Monitors trainee's brain-wave activity: if EEG signal indicates attention is waning, system increases difficulty of game, forcing trainee to devote more attention to it. Game designed to make trainees want to win and, in so doing, learn to pay attention for longer times.

Pope, Alan T.↗
NASA NTRS1999

Electroencephalographic imaging of higher brain function

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Non-NASA Center↗
NASA NTRS2006

User Interactive Software for Analysis of Human Physiological Data

Ambulatory physiological monitoring has been used to study human health and performance in space and in a variety of Earth-based environments (e.g., military aircraft, armored vehicles, small groups in isolation, and patients). Large, multi-channel data files are typically recorded in these environments, and these files often require the removal of contaminated data prior to processing and analyses. Physiological data processing can now be performed with user-friendly, interactive software developed by the Ames Psychophysiology Research Laboratory. This software, which runs on a Windows platform, contains various signal-processing routines for both time- and frequency- domain data analyses (e.g., peak detection, differentiation and integration, digital filtering, adaptive thresholds, Fast Fourier Transform power spectrum, auto-correlation, etc.). Data acquired with any ambulatory monitoring system that provides text or binary file format are easily imported to the processing software. The application provides a graphical user interface where one can manually select and correct data artifacts utilizing linear and zero interpolation and adding trigger points for missed peaks. Block and moving average routines are also provided for data reduction. Processed data in numeric and graphic format can be exported to Excel. This software, PostProc (for post-processing) requires the Dadisp engineering spreadsheet (DSP Development Corp), or equivalent, for implementation. Specific processing routines were written for electrocardiography, electroencephalography, electromyography, blood pressure, skin conductance level, impedance cardiography (cardiac output, stroke volume, thoracic fluid volume), temperature, and respiration

Cowings, Patricia S.↗
NASA NTRS2015

Multimodal Neurodiagnostic Tool for Exploration Missions

Linea Research Corporation has developed a neurodiagnostic tool that detects behavioral stress markers for astronauts on long-duration space missions. Lightweight and compact, the device is unobtrusive and requires minimal time and effort for the crew to use. The system provides a real-time functional imaging of cortical activity during normal activities. In Phase I of the project, Linea Research successfully monitored cortical activity using multiparameter sensor modules. Using electroencephalography (EEG) and functional near-infrared spectroscopy signals, the company obtained photoplethysmography and electrooculography signals to compute the heart rate and frequency of eye movement. The company also demonstrated the functionality of an algorithm that automatically classifies the varying degrees of cognitive loading based on physiological parameters. In Phase II, Linea Research developed the flight-capable neurodiagnostic device. Worn unobtrusively on the head, the device detects and classifies neurophysiological markers associated with decrements in behavior state and cognition. An automated algorithm identifies key decrements and provides meaningful and actionable feedback to the crew and ground-based medical staff.

Lee, Yong Jin↗
NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS2017

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS2023

Reconfigurations in Brain Networks Upon Awakening From Slow Wave Sleep: Interventions and Implications in Neural Communication

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.

EEG↗