Multivariate analysis of EEG: predicting cognition on the basis of frequency decomposition, inter-electrode correlation, coherence, cross phase and cross power

Pleydell-Pearce, Christopher W.; Whitecross, Sharron E.; Dickson, Blair

doi:10.1109/hicss.2003.1174299

Cited by 15 publications

(19 citation statements)

References 15 publications

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“…The exclusion of ECG, respiration, accelerometer and photocell data indicates that there are large differences between relaxed wakefiilness and performance on the WCT. We wish to stress that the number of significant differences is far greater than would be expected on the basis of chance (see Pleydell-Pearce et al, 2003, for similar findings and discussion). These findings do not, of course, guarantee that these same variables will discriminate levels of task demand.…”

Section: Physiological Measures Of Executive Loadmentioning

confidence: 99%

“…Two such gauges are reported here: one using the physiological measure approach we have explained elsewhere (Pleydell-Pearce et al, 2003); the second based upon tracking of behavior using the CMTT. All events passed from the WCT were analyzed for their intrinsic meaning, in terms of the cognitive processes they represent.…”

Section: Executive Loadmentioning

confidence: 99%

“…Bespoke predictions are based on coefficients or 'weights' that are derived for each participant, on the basis of calibration data and previous encounters with the individual participant (e.g. Pleydell-Pearce et al, 2003). Generic predictions are based upon weights that are derived fi-om across-participant analyses.…”

Section: The Gauge Conceptmentioning

confidence: 99%

“…This system is known as the Task Taxonomy and was first described by Pleydell-Pearce et al, (2000). The Cognition Monitor Task Taxonomy (CMTT) was initially implemented within the DERA/QinetiQ Cockpit Program (Taylor et al, 2001).…”

Section: Overview Of Civi Architecturementioning

confidence: 99%

See 3 more Smart Citations

DARPA Augmented Cognition Technical Integration Experiment (TIE)

John¹,

Kobus²,

Morrison³

2003

PsycEXTRA Dataset

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Section: Physiological Measures Of Executive Loadmentioning

confidence: 99%

Section: Executive Loadmentioning

confidence: 99%

Section: The Gauge Conceptmentioning

confidence: 99%

Section: Overview Of Civi Architecturementioning

confidence: 99%

See 2 more Smart Citations

DARPA Augmented Cognition Technical Integration Experiment (TIE)

John¹,

Kobus²,

Morrison³

2003

PsycEXTRA Dataset

View full text Add to dashboard Cite

“…Pleydell-Pearce (2003) achieved a classification accuracy of 72% for the discrimination of low versus high task demand in subject and session dependent experiments and 71% in subject independent experiments. Task demand assessment has also been done on data from other modalities, including muscular activity (Pleydell-Pearce, 2003), blood hemodynamics (Izzetoglu, 2004), and pupil diameter (Iqbal, 2004). Reasonable results could be achieved with all three modalities.…”

Section: Task Demand and Vigilancementioning

confidence: 99%

Determine Task Demand From Brain Activity

Honal¹,

Schultz

2008

Proceedings of the First International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:Our society demands ubiquitous mobile devices that offer seamless interaction with everybody, everything, everywhere, at any given time. However, the effectiveness of these devices is limited due to their lack of situational awareness and sense for the users' needs. To overcome this problem we develop intelligent transparent human-centered systems that sense, analyze, and interpret the user's needs. We implemented learning approaches that derive the current task demand from the user's brain activity by measuring the electroencephalogram. Using Support Vector Machines we can discriminate high versus low task demand with an accuracy of 92.2% in session dependent experiments, 87.1% in session independent experiments, and 80.0% in subject independent experiments. To make brain activity measurements less cumbersome, we built a comfortable headband with which we achieve 69% classification accuracy on the same task.

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Stress and its Impact on the Neurocognitive Performance of Australian Nurses

Lees

Lal

2016

Stress and Health

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Nurses function inside a particularly stressful occupation that requires the provision of continuous care to individuals who are often in great need. Stress has been shown to impair performance and specifically shown to impair nursing quality. However, we do not yet know how stress influences the cognitive performance of nurses, and hence, the present study investigated the associations between stress and cognitive performance in nurses using electroencephalography and administered cognitive assessments. Thirty-six nurses (34 women) of mean age 37.77 ± 11.40 years were recruited. Stress was examined using the Lifestyle Appraisal Questionnaire. Broad spectrum electroencephalogram activity at positions Fp1, Fp2, C3 and C4 was recorded for a 5-min baseline and active phase to physiologically assess cognitive performance. Additionally, the Mini-Mental State Exam and Cognistat were also used to measure cognitive performance. Assessed cognitive performance was not associated to stress, however, lifestyle factors, as well as a number of the examined cognitive electroencephalographic variables including changes in theta, alpha activity and gamma reactivity were. Definitively determining how stress affects the cognitive performance of nurses requires additional research; the present study forms a foundation from which future research can further expand the examination of stress exposure in nurses. Copyright © 2016 John Wiley& Sons, Ltd.

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Multivariate analysis of EEG: predicting cognition on the basis of frequency decomposition, inter-electrode correlation, coherence, cross phase and cross power

Cited by 15 publications

References 15 publications

DARPA Augmented Cognition Technical Integration Experiment (TIE)

DARPA Augmented Cognition Technical Integration Experiment (TIE)

Determine Task Demand From Brain Activity

Stress and its Impact on the Neurocognitive Performance of Australian Nurses

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