Respiratory sinus arrhythmia as a measure of cognitive workload

Muth, Eric R.; Moss, Jason D.; Rosopa, Patrick J.; Salley, James N.; Walker, Alexander D.

doi:10.1016/j.ijpsycho.2011.10.011

Cited by 33 publications

(18 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies demonstrate that both HR and HRV-related indexes can effectively reflect the mental workload level of flight. 23,24 However, the results of the present study show that HR detection might not be able to effectively reflect the mental workload, while HRV detection could be able to effectively reflect the mental workload, which is consistent with the conclusions drawn by Muth et al 17 This might have been caused by the fact that the main factor influencing HR is physical workload, while the experimental task of the present study is to induce the occurrence of mental workload. The present study also explores the differentiation of the forementioned six time-domain indexes of HRV among different flight taskrelated mental workloads, the results of which showed that only the time-domain index SDNN was significantly sensitive to the change of flight task-related mental workload, as specifically demonstrated by the progressive decrease of the SDNN value with the increase of mental workload.…”

Section: Differentiation Of the Three Types Of Evaluation Indexes Amosupporting

confidence: 83%

“…Relevant studies showed that both heart rate and heart rate variability (HRV) indexes can effectively reflect the different levels of mental workload. [15][16][17] However, there was some limitation for using frequency-domain index of HRV to reflect the physiological change, because it is affected by the length of data extraction period, and the essence of physiological change reflected still needs further study. 18 Besides, relevant studies also indicated that within a certain period of time (5 min), there is a significant correlation between time-domain related indexes and frequency-domain related indexes in R-R interval.…”

Section: Ecg Data Recordingmentioning

confidence: 99%

See 1 more Smart Citation

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

Zongmin

Zhuang

Wanyan

et al. 2014

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals (SDNN) significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future. ª 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

show abstract

Section: Differentiation Of the Three Types Of Evaluation Indexes Amosupporting

confidence: 83%

Section: Ecg Data Recordingmentioning

confidence: 99%

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

Zongmin

Zhuang

Wanyan

et al. 2014

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

show abstract

“…Moreover, the usefulness of the respiratory signals has been studied to verify the correlation with subjective measures. Muth et al (2012) have shown that the respiratory sinus arrhythmia (RSA) index can predict scores on the NASA-Task Load Index questionnaire. The authors tested RSA as a real-time measure of cognitive workload in participants completing a performance-based selection test developed by the U.S. Navy.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular correlates of emotional state, cognitive workload and time-on-task effect during a realistic flight simulation

Hidalgo‐Muñoz

Mouratille

Matton

et al. 2018

International Journal of Psychophysiology

View full text Add to dashboard Cite

In aviation, emotion and cognitive workload can considerably increase the probability of human error. An accurate online physiological monitoring of pilot's mental state could prevent accidents. The heart rate (HR) and heart rate variability (HRV) of 21 private pilots were analysed during two realistic flight simulator scenarios. Emotion was manipulated by a social stressor and cognitive workload with the difficulty of a secondary task. Our results confirmed the sensitivity of the HR to cognitive demand and training effects, with increased HR when the task was more difficult and decreased HR with training (time-on-task). Training was also associated with an increased HRV, with increased values along the flight scenario time course. Finally, the social stressor seemed to provoke an emotional reaction that enhanced motivation and performance on the secondary task. However, this was not reflected by the cardiovascular activity.

show abstract

“…More precisely, there is strong evidence that particularly the high-frequency (HF-) HRV component in the range of 0.15 to 0.4 Hz reflects the modulation of vagus nerve activity (Berntson et al, 1997, Malik and Camm, 1993). In addition to physiological adaptation processes, numerous studies have shown that vagal activity is also sensitive to cognitive processes and attenuates during cognitively demanding tasks, e.g., during driving (Stuiver et al, 2014) and aviation simulation (Rowe et al, 1998, Muth et al, 2012), a visual attention test (Duschek et al, 2009) or a working memory and sustained perceptual attention task (Overbeek et al, 2014). A suppression of vagal activity induced by high cognitive demand results in lower adaptation to variations in blood pressure and thus less heart rate variability (Hogervorst et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Heart rate variability predicts decline in sensorimotor rhythm control

Nann

Haslacher

Colucci

et al. 2021

Preprint

View full text Add to dashboard Cite

Voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g., in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. It would thus be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline during voluntary control of SMR-ERD across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 minutes each. During the second run, task difficulty was gradually increased. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the first run, during the second run, both measures declined over time at high correlation (performance: -0.61%/10s, p = 0; HRV: -0.007ms/10s, p < .001). We found that HRV Granger-caused BCI control performance (p < .001) exhibited predictive characteristics of HRV on an individual participant level. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g., to individualize and optimize assistive BCI systems in stroke.

show abstract

Respiratory sinus arrhythmia as a measure of cognitive workload

Cited by 33 publications

References 30 publications

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

Cardiovascular correlates of emotional state, cognitive workload and time-on-task effect during a realistic flight simulation

Heart rate variability predicts decline in sensorimotor rhythm control

Contact Info

Product

Resources

About