Probing ECG-based mental state monitoring on short time segments

Roy, Raphaëlle N.; Charbonnier, Sylvie; Campagne, Aurélie

doi:10.1109/embc.2013.6611071

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…Such system could intervene in real-time before the operator become overloaded while performing safety-critical tasks [2]. Different systems to estimate the mental workload has been previously presented by using EEG or HR or other biometric signals [3], [4], [5]. However, all of these works presented the use of a single modality each time (e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards a multimodal bioelectrical framework for the online mental workload evaluation

Aricò

Borghini

Graziani

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this study, a framework able to classify online different levels of mental workload induced during a simulated flight by using the combination of the Electroencephalogram (EEG) and the Heart Rate (HR) biosignals has been proposed. Ten healthy subjects were involved in the experimental protocol, performing the NASA - Multi Attribute Task Battery (MATB) over three different difficulty levels in order to simulate three classic showcases in a flight scene (cruise flight phase, flight level maintaining, and emergencies). The analyses showed that the proposed system is able to estimate online the mental workload of the subjects over the three different conditions reaching a high discriminability (p<.05). In addition, it has been found that the classification parameters remained stable within a week, without recalibrating the system with new parameters.

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Section: Introductionmentioning

confidence: 99%

Towards a multimodal bioelectrical framework for the online mental workload evaluation

Aricò

Borghini

Graziani

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…The literature review for this work indicates more candidate metrics than those tested here. In the present study, RR and RR SD, were demonstrated to be successful as online indicators of working memory load, which is an encouragement for testing the remaining metrics, in particular frequency analyses such as: low-frequency (LF), highfrequency (HF), and LF/HF frequency-power [12], [13], [16], [27], [28]. In addition, the results of the candidate identification indicated other metrics that may be of interest that have not appeared in the literature yet, namely the derivative of the RR SD stream and the first and second derivatives of the RR stream.…”

Section: Discussionmentioning

confidence: 96%

“…A working memory (WM) span task is a popular and well-documented type of cognitive psychology test [19] and has already been used for similar heart-rhythm-cognitiveload tests [16], producing noticeable effects in cardiac activity, albeit in offline block analysis. It involves presenting participants with a sequence of digits or letters that they are subsequently required to recall in the presented order.…”

Section: Span Task Experimentsmentioning

confidence: 99%

“…In particular, they evaluate ECG-based time-domain metrics to detect participants' stress during everyday life activities. Yet, even though the inclusion of such metrics was proposed [10] to infer cognitive states, no existing ECG-based online tool for cognitive workload evaluation has been developed yet and experimentally evaluated, to the authors' best knowledge [12], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

“…Particular attention is given here to online assessment of Heart Rate Variability (HRV), which has been shown to be correlated with cognitive workload [3], [10], [16], [17], [18]. More precisely, this metric has been shown to correlate inversely with cognitive workload, although that correlation diminishes with subject fatigue or "time on task" [16]. The temporal HRV metric can be defined as the standard deviation of the IBI series:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online ECG-based Features for Cognitive Load Assessment

Chanel

Wilson

Scannella

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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This study was concerned with the development and testing of online cognitive-load monitoring methods by means of a working-memory experiment using electrocardiogram (ECG) analyses for future applications in mixedinitiative human-machine interaction (HMI). To this end, we first identified potentially reliable cognitive-workload-related cardiac metrics and algorithms for online processing. We then compared our online results to those conventionally obtained with state-of-the-art offline methods. Finally, we evaluated the possibility of classifying low versus high working-memory load using different classification algorithms. Our results show that both offline and online methods reliably estimate the workload associated with a multi-level working-memory task at the group level, whether it is with the heart rhythm or the heart rate variation (standard deviation of the RR interval). Moreover, we found significant working-memory load classification accuracy using both two-dimensional linear discriminant analyses (LDA) or a support vector machine (SVM). We hence argue that our online algorithm is reliable enough to provide online electrocardiographic metrics as a tool for real-life workload evaluation and can be a valuable feature for mixed-initiative systems.

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