Phase coherence of the alpha rhythm during photic blocking
Alpha rhythm phase coherence during photic blocking, examining pacemaker model
Goldstein, S.↗
SEARCH · Search NASA
Results for “Alpha Rhythm”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Effect of immobilization on the EEG of the baboon. Comparison with telemetry results from unrestricted animals
The EEG of the baboon was studied under two very different sets of conditions: 37 were totally immobolized while 12 were studied in their free movements with 4 channel telemetry. For the immobilzed, 3 stages were described: (1) activation, record desynchronized; (2) rest with 13-15 cm/sec rhythm, like the human alpha rhythm stage but with eyes open or closed; (3)relaxation with a decrease in 13-15 rhythm and the appearance of 5-7 cm/sec theta waves, eyelids closed, animal apparently sleeping. For the free animals the rest stage appeared when the animal's attention was not directed anywhere and there was no relaxation stage. It is concluded that the EEG pattern of the immobilized animal that was described as the "relaxation" stage really represents a special functional state which one must distinguish clearly from the physiological stages of sleep.
Bert, J.↗
Application of frequency discrimination technique to the analysis of electroencephalographic signals
A frequency discrimination technique is realized by an inexpensive frequency discriminator for direct on-line measurement of the entrainment phenomenon in EEG as entrained by the frequency of a sensory stimulus. The use of this device for detecting the presence or absence of the stimulus effect and the measurement of the time delays in the entrainment is demonstrated. The EEG signal is first filtered by a narrow-band filter with center frequency about the alpha-rhythm of the individual. It is then reasonable to consider the alpha signal as narrow-band random process with the alpha frequency f sub alpha as the mean frequency of the spectral band.
Lai, D. C.↗
A natural basis for efficient brain-actuated control
The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.
NASA Discipline Space Human Factors↗
Measures and Models for Estimating and Predicting Cognitive Fatigue
We analyzed EEG and ERPs in a fatiguing mental task and created statistical models for single subjects. Seventeen subjects (4 F, 18-38 y) viewed 4-digit problems (e.g., 3+5-2+7=15) on a computer, solved the problems, and pressed keys to respond (intertrial interval = 1 s). Subjects performed until either they felt exhausted or three hours had elapsed. Re- and post-task measures of mood (Activation Deactivation Adjective Checklist, Visual Analogue Mood Scale) confirmed that fatigue increased and energy decreased over time. We tested response times (RT); amplitudes of ERP components N1, P2, P300, readiness potentials; and amplitudes of frontal theta and parietal alpha rhythms for change as a function of time. For subjects who completed 3 h (n=9) we analyzed 12 15-min blocks. For subjects who completed at least 1.5 h (n=17), we analyzed the first-, middle-, and last 100 error-free trials. Mean RT rose from 6.7 s to 8.5 s over time. We found no changes in the amplitudes of ERP components. In both analyses, amplitudes of frontal theta and parietal alpha rose by 30% or more over time. We used 30-channel EEG frequency spectra to model the effects of time in single subjects using a kernel partial least squares classifier. We classified 3.5s EEG segments as being from the first 100 or the last 100 trials, using random sub-samples of each class. Test set accuracies ranged from 63.9% to 99.6% correct. Only 2 of 17 subjects had mean accuracies lower than 80%. The results suggest that EEG accurately classifies periods of cognitive fatigue in 90% of subjects.
Trejo, Leonard J.↗
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.↗
Separation of circadian and wake duration-dependent modulation of EEG activation during wakefulness
The separate contribution of circadian rhythmicity and elapsed time awake on electroencephalographic (EEG) activity during wakefulness was assessed. Seven men lived in an environmental scheduling facility for 4 weeks and completed fourteen 42.85-h 'days', each consisting of an extended (28.57-h) wake episode and a 14.28-h sleep opportunity. The circadian rhythm of plasma melatonin desynchronized from the 42.85-h day. This allowed quantification of the separate contribution of circadian phase and elapsed time awake to variation in EEG power spectra (1-32 Hz). EEG activity during standardized behavioral conditions was markedly affected by both circadian phase and elapsed time awake in an EEG frequency- and derivation-specific manner. The nadir of the circadian rhythm in alpha (8-12 Hz) activity in both fronto-central and occipito-parietal derivations occurred during the biological night, close to the crest of the melatonin rhythm. The nadir of the circadian rhythm of theta (4.5-8 Hz) and beta (20-32 Hz) activity in the fronto-central derivation was located close to the onset of melatonin secretion, i.e. during the wake maintenance zone. As time awake progressed, delta frequency (1-4.5 Hz) and beta (20-32 Hz) activity rose monotonically in frontal derivations. The interaction between the circadian and wake-dependent increase in frontal delta was such that the intrusion of delta was minimal when sustained wakefulness coincided with the biological day, but pronounced during the biological night. Our data imply that the circadian pacemaker facilitates frontal EEG activation during the wake maintenance zone, by generating an arousal signal that prevents the intrusion of low-frequency EEG components, the propensity for which increases progressively during wakefulness.
Non-NASA Center↗
Analysis and visualization of single-trial event-related potentials
In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.
Non-NASA Center↗
Biocybernetic system evaluates indices of operator engagement in automated task
A biocybernetic system has been developed as a method to evaluate automated flight deck concepts for compatibility with human capabilities. A biocybernetic loop is formed by adjusting the mode of operation of a task set (e.g., manual/automated mix) based on electroencephalographic (EEG) signals reflecting an operator's engagement in the task set. A critical issue for the loop operation is the selection of features of the EEG to provide an index of engagement upon which to base decisions to adjust task mode. Subjects were run in the closed-loop feedback configuration under four candidate and three experimental control definitions of an engagement index. The temporal patterning of system mode switching was observed for both positive and negative feedback of the index. The indices were judged on the basis of their relative strength in exhibiting expected feedback control system phenomena (stable operation under negative feedback and unstable operation under positive feedback). Of the candidate indices evaluated in this study, an index constructed according to the formula, beta power/(alpha power + theta power), reflected task engagement best.
Cerebral Cortex/physiology↗
EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss
The aim of this study was to quantify the associations between slow eye movements (SEMs), eye blink rate, waking electroencephalogram (EEG) power density, neurobehavioral performance, and the circadian rhythm of plasma melatonin in a cohort of 10 healthy men during up to 32 h of sustained wakefulness. The time course of neurobehavioral performance was characterized by fairly stable levels throughout the first 16 h of wakefulness followed by deterioration during the phase of melatonin secretion. This deterioration was closely associated with an increase in SEMs. Frontal low-frequency EEG activity (1-7 Hz) exhibited a prominent increase with time awake and little circadian modulation. EEG alpha activity exhibited circadian modulation. The dynamics of SEMs and EEG activity were phase locked to changes in neurobehavioral performance and lagged the plasma melatonin rhythm. The data indicate that frontal areas of the brain are more susceptible to sleep loss than occipital areas. Frontal EEG activity and ocular parameters may be used to monitor and predict changes in neurobehavioral performance associated with sleep loss and circadian misalignment.
Non-NASA Center↗
Effects of square-wave and simulated natural light-dark cycles on hamster circadian rhythms
Circadian rhythms of activity (Act) and body temperature (Tb) were recorded from male Syrian hamsters under square-wave (LDSq) and simulated natural (LDSN, with dawn and dusk transitions) light-dark cycles. Light intensity and data sampling were under the synchronized control of a laboratory computer. Changes in reactive and predictive onsets and offsets for the circadian rhythms of Act and Tb were examined in both lighting conditions. The reactive Act onset occurred 1.1 h earlier (P < 0.01) in LDSN than in LDSq and had a longer alpha-period (1.7 h; P < 0.05). The reactive Tb onset was 0.7 h earlier (P < 0.01) in LDSN. In LDSN, the predictive Act onset advanced by 0.3 h (P < 0.05), whereas the Tb predictive onset remained the same as in LDSq. The phase angle difference between Act and Tb predictive onsets decreased by 0.9 h (P < 0.05) in LDSN, but the offsets of both measures remained unchanged. In this study, animals exhibited different circadian entrainment characteristics under LDSq and LDSN, suggesting that gradual and abrupt transitions between light and dark may provide different temporal cues.
NASA Discipline Regulatory Physiology↗
Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics
We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.
NASA Discipline Cardiopulmonary↗
Circadian variation of EEG power spectra in NREM and REM sleep in humans: dissociation from body temperature
In humans, EEG power spectra in REM and NREM sleep, as well as characteristics of sleep spindles such as their duration, amplitude, frequency and incidence, vary with circadian phase. Recently it has been hypothesized that circadian variations in EEG spectra in humans are caused by variations in brain or body temperature and may not represent phenomena relevant to sleep regulatory processes. To test this directly, a further analysis of EEG power spectra - collected in a forced desynchrony protocol in which sleep episodes were scheduled to a 28-h period while the rhythms of body temperature and plasma melatonin were oscillating at their near 24-h period - was carried out. EEG power spectra were computed for NREM and REM sleep occurring between 90-120 and 270-300 degrees of the circadian melatonin rhythm, i.e. just after the clearance of melatonin from plasma in the 'morning' and just after the 'evening' increase in melatonin secretion. Average body temperatures during scheduled sleep at these two circadian phases were identical (36.72 degrees C). Despite identical body temperatures, the power spectra in NREM sleep were very different at these two circadian phases. EEG activity in the low frequency spindle range was significantly and markedly enhanced after the evening increase in plasma melatonin as compared to the morning phase. For REM sleep, significant differences in power spectra during these two circadian phases, in particular in the alpha range, were also observed. The results confirm that EEG power spectra in NREM and REM sleep vary with circadian phase, suggesting that the direct contribution of temperature to the circadian variation in EEG power spectra is absent or only minor, and are at variance with the hypothesis that circadian variations in EEG power spectra are caused by variations in temperature.
Non-NASA Center↗