Topographical changes in the ongoing EEG related to the difficulty of mental tasks

Gundel, A.; Wilson, Glenn F.

doi:10.1007/bf01129966

Cited by 190 publications

(126 citation statements)

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“…Decreased alpha in the high-load task is consistent with the notion that the amplitude of this signal is inversely related to the proportion of cortical neurons involved with task performance. Similar patterns of effort-related modulation of the EEG have been found in numerous other investigations (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Although spectral components of healthy adults' EEG have been shown to be sensitive to the mental effort associated with computer-based laboratory tasks, few data exist concerning whether the EEGs of juvenile subjects playing commercial video games will exhibit the similar patterns.…”

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confidence: 74%

Mental Effort‐Related EEG Modulation During Video‐Game Play: Comparison Between Juvenile Subjects with Epilepsy and Normal Control Subjects

et al. 1999

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Summary:Purpose: This study investigated the effects of mental effort exerted during video-game play on features of the EEG in juvenile subjects diagnosed with seizure disorders and in age-matched clinically healthy subjects.Methods: EEG was recorded from 14 children (9-15 years old) as they played a video game, watched another person playing a video-game, and sat quietly with their eyes open. Seven of the subjects had been clinically diagnosed with seizure disorders, three of whom had also exhibited photosensitivity.Results: Three spectral components of the EEG showed cognitive load-related modulation. The amplitude of a frontal midline theta (6-7 Hz) signal increased with video-game play relative to the watching and eyes open resting conditions. A posterior alpha band (9-12 Hz) signal was attenuated during the playing and the watching conditions relative to the resting condition. A central mu (10-13 Hz) rhythm was attenuated during the game-playing condition. No significant differences were found between the patient and control groups for any of these features. Incidence of epileptiform events did not discriminate test conditions in the children with epilepsy.Conclusions: The results from this small sample suggest that video-game play tends to produce similar responses from children with epilepsy and in healthy control subjects. These responses in the juvenile population are similar to responses elicited by increased mental load in normal adult populations.

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confidence: 74%

Mental Effort‐Related EEG Modulation During Video‐Game Play: Comparison Between Juvenile Subjects with Epilepsy and Normal Control Subjects

et al. 1999

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“…Heart rate is useful to determine the operator's global response to task demands (Wilson & Eggemeier, 1991). The electroencephalogram (EEC) provides useful information about both high workload and inattention (Gundel & Wilson, 1992;Kramer, 1991;Steman & Mann, 1995;Wilson & Eggemeier, 1991). EEG measures have been used to classify patients with regard to types of neuropathy and psychiatric disorders using linear statistical techniques (John, Pricep, Fridman, & Easton, 1988) and artificial neural networks (ANNs; Kloppel, 1994).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks

2003

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The public reporting burden for this coliection of infornnation is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of infonmation, including suggestions for reducing this burden, PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)Air Force SPONSOR/MONITOR'S REPORT NUMBER(S)Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES 20040324 044 ABSTRACTThe functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficultv. and high task difficulty conditions, respectivelv. The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this r...

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“…Klimesch has suggested that theta synchronisation reflects the introduction of cortical activation via cortico-hippocampal feedback loops, and has demonstrated that task-related increases in theta power are related to the successful encoding of new information in episodic memory [31,173]. Increases in theta are typically associated with increases in mental workload as defined by task difficulty and stimulus complexity [184], whereas decreased theta is found to be associated with incorrect responses on a signal detection task [185] or when tasks become more familiar or easier [186].…”

Section: Theta Activity (Indexed By Theta Spectral Components)mentioning

confidence: 99%

“…Frontal midline theta has been reported to increase with increased memory load and workload in general [186,[189][190][191], mental concentration [165,166,[192][193][194], focused attention [129,184,195] and serves a response controlling function [151].…”

Section: Theta Activity (Indexed By Theta Spectral Components)mentioning

confidence: 99%

Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology~!2009-11-10~!2010-02-22~!2010-09-08~!

Fingelkurts¹

2010

TONIJ

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Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a "static" picture which cannot reflect adequately the underlying neurodynamic. A relatively new promising area in the study of EEG is based on reducing the signal to elementary short-term spectra of various types in accordance with the number of types of EEG stationary segments instead of using averaged power spectrum for the whole EEG. It is suggested that the various perceptual and cognitive operations associated with a mental or behavioural condition constitute a single distinguishable neurophysiological state with a distinct and reliable spectral pattern. In this case, one type of short-term spectral pattern may be considered as a single event in EEG phenomenology. To support this assumption the following issues are considered in detail: (a) the relations between local EEG short-term spectral pattern of particular type and the actual state of the neurons in underlying network and a volume conduction; (b) relationship between morphology of EEG short-term spectral pattern and the state of the underlying neurodynamical system i.e. neuronal assembly; (c) relation of different spectral pattern components to a distinct physiological mechanism; (d) relation of different spectral pattern components to different functional significance; (e) developmental changes of spectral pattern components; (f) heredity of the variance in the individual spectral pattern and its components; (g) intra-individual stability of the sets of EEG short-term spectral patterns and their percent ratio; (h) discrete dynamics of EEG short-term spectral patterns. Functional relevance (consistency) of EEG short-term spectral patterns in accordance with the changes of brain functional state, cognitive task and with different neuropsychopathologies is demonstrated.

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Topographical changes in the ongoing EEG related to the difficulty of mental tasks

Cited by 190 publications

References 23 publications

Mental Effort‐Related EEG Modulation During Video‐Game Play: Comparison Between Juvenile Subjects with Epilepsy and Normal Control Subjects

Mental Effort‐Related EEG Modulation During Video‐Game Play: Comparison Between Juvenile Subjects with Epilepsy and Normal Control Subjects

Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks

Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology~!2009-11-10~!2010-02-22~!2010-09-08~!

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