Applying Real Time Physiological Measures of Cognitive Load to Improve Training

Coyne, Joseph T.; Baldwin, Carryl L.; Cole, Anna; Sibley, Ciara; Roberts, Daniel M.

doi:10.1007/978-3-642-02812-0_55

Cited by 38 publications

(23 citation statements)

References 27 publications

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“…For patients, calibration and training of the BCI system may also increase their cognitive load, creating discomfort and fatigue. To assess cognitive load, a subjective rating system called NASA Task Load Index can be used (65). Additionally, physiological measures such as eye-activity, EEG ERP and spectral features could also be used to measure cognitive load objectively (65).…”

Section: Resultsmentioning

confidence: 99%

Performance measurement for brain–computer or brain–machine interfaces: a tutorial

et al. 2014

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Objective Brain-Computer Interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This manuscript contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main Results Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their application to encourage uniform application between laboratories. Significance Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.

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Section: Resultsmentioning

confidence: 99%

Performance measurement for brain–computer or brain–machine interfaces: a tutorial

et al. 2014

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“…The majority of these studies used pupil dilation as a measure of cognitive load (Marshall, 2002;Marshall et al, 2003;St John et al, 2003;Taylor et al, 2003;Raley et al, 2004;St. John et al, 2004;Johnson et al, 2005;Mathan et al, 2005;Russo et al, 2005;Ververs et al, 2005;De Greef et al, 2007;Coyne et al, 2009), while only few attempted to actually develop the technical infrastructure for real-time assistance via gaze location (Barber et al, 2008), though none produced results using gaze location. 4.5.2.…”

Section: Augmented Cognition Human-agent Human-robot Human-swarm Imentioning

confidence: 99%

Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location

Taylor

Bilgrien

et al. 2015

Front. ICT

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Objective: We aimed to elucidate how our domain-general cuing algorithm improved multitasking performance and changed behavioral strategies in human operators.Background: Though many gaze-control systems have been designed, previous realtime gaze-aware assistance systems were not both successful and domain-general. It is largely unknown what constitutes optimal search efficiency using the eyes, or ideal control using the mouse. It is unclear what the best coordinating strategies are between these two modalities. Our previously developed closed-loop multitasking aid drastically improved multitasking performance, though the behavioral mechanisms through which it acted were unknown. Methods:We performed in-depth analyses and generated novel eye tracking and mouse movement measures, to explore the complex effects of our helpful system on gaze and motor behavior.Results: Our overlay cuing algorithm improved control efficiency and reduced wellknown biases in search patterns. This system also reduced micromanaging behavior, with humans rationally relying more on imperfect automation in experimental assistance cue conditions. We showed that mouse and gaze were more independently specialized in the helpful cuing condition than in control conditions. Specifically, with our aid, the gaze performed more global movement, and the mouse performed more local clustered movement. Further, the gaze shifted toward search over processing with the helpful cuing system. We also illustrated a relationship between the mouse and the gaze, such that in these studies, "the hand was quicker than the eye." Conclusion:Overall, results suggested that our cuing system improved performance and reduced short-term working memory load on humans by delegating it to the computer in real time. Further, it reduced the number of required repeated decisions by an estimate of about one per second. It also enabled the gaze to specialize for improved visual search behavior, and the mouse to specialize for improved control.

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“…An objective method for determining the effect of task load on performance is useful particularly when such information is required in real-time as when the load changes quickly (Coyne et al, 2009;Chen et al, 2012;Hancock et al, 2013). However, the effects of task loading are inherently multi-dimensional and go beyond cognitive and mental effort (Young et al, 2015) or physical effort (Borg, 1990).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Study of the Effects of Varying Task Load Utilizing EEG, GSR and Eye-Tracking

Born

Ramachandran

Pinto

et al. 2019

Preprint

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Objective. The effect of task load on performance is investigated by simultaneously collecting multi-modal physiological data and participant response data. Periodic response to a questionnaire is also obtained. The goal is to determine combinations of modalities that best serve as predictors of task performance.Approach. A group of participants performed a computer-based visual search task mimicking postal code sorting. A five-digit number had to be assigned to one of six different non-overlapping numeric ranges. Trials were presented in blocks of progressively increasing task difficulty. The participants' responses were collected simultaneously with 32 channels of electroencephalography (EEG) data, eye-tracking data, and Galvanic Skin Response (GSR) data. The NASA Task-Load-Index self-reporting instrument was administered at discrete time points in the experiment. Main results. Low beta frequency EEG waves (12.5-18 Hz) were more prominent as cognitive task load increased, with most activity in frontal and parietal regions. These were accompanied by more frequent eye blinks and increased pupillary dilation. Blink duration correlated strongly with task performance. Phasic components of the GSR signal were related to cognitive workload, whereas tonic components indicated a more general state of arousal. Subjective data (NASA TLX) as reported by the participants showed an increase in frustration and mental workload. Based on one-way ANOVA, EEG and GSR provided the most reliable correlation to perceived workload level and were the most informative measures (taken together) for performance prediction.Significance. Numerous modalities come into play during task-related activity. Many of these modalities can provide information on task performance when appropriately grouped. This study suggests that while EEG is a good predictor of task performance, additional modalities such as GSR increase the likelihood of more accurate predictions. Further, in controlled laboratory conditions, the most informative or minimum number of modalities can be isolated for monitoring in real work environments.

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Applying Real Time Physiological Measures of Cognitive Load to Improve Training

Cited by 38 publications

References 27 publications

Performance measurement for brain–computer or brain–machine interfaces: a tutorial

Performance measurement for brain–computer or brain–machine interfaces: a tutorial

Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location

Multimodal Study of the Effects of Varying Task Load Utilizing EEG, GSR and Eye-Tracking

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