Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

Art experience is not solely the observation of artistic objects, but great relevance is also placed on the environment in which the art experience takes place, often in museums and galleries. Interestingly, in the last few years, the introduction of some forms of virtual reality (VR) in museum contexts has been increasing. This has solicited enormous research interest in investigating any eventual differences between looking at the same artifact either in a real context (e.g. a museum) and in VR. To address such a target, a neuroaesthetic study was performed in which electroencephalography (EEG) and autonomic signals (heart rate and skin conductance) were recorded during the observation of the Etruscan artifact “Sarcophagus of the Spouses”, both in the museum and in a VR reproduction. Results from EEG analysis showed a higher level of the Workload Index during observation in the museum compared to VR (p = 0.04), while the Approach–Withdrawal Index highlighted increased levels during the observation in VR compared to the observation in the museum (p = 0.03). Concerning autonomic indices, the museum elicited a higher Emotional Index response than the VR (p = 0.03). Overall, preliminary results suggest a higher engagement potential of the museum compared to VR, although VR could also favour higher embodiment than the museum.

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“…The device has been validated and is capable of recording the EEG signal extremely accurately [ 26 ]. The sampling frequency was 250 (Hz).…”

Section: Methodsmentioning

confidence: 99%

Virtual and Reality: A Neurophysiological Pilot Study of the Sarcophagus of the Spouses

et al. 2023

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“…Regarding stress measurement, the literature proved that there is a correlation between cortisol and brain activity in beta (Seo and Lee, 2010 ). A stress neurometric based on parietal brain activity in the high beta band was effectively tested during laboratory multitasking and was validated during realistic driving, proving that it is possible to have reliable stress measurements using the following (Sciaraffa et al, 2022 ):…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

Sciaraffa¹,

Flumeri

Germano³

et al. 2022

Front. Hum. Neurosci.

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Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

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“…Several studies have demonstrated the ability to classify mental workload associated with various laboratory tasks (e.g. N-back [13], mental arithmetic [14], Sternberg memory [15], auditory oddball target paradigm [16,17], visual search [17], and a laboratory version of air traffic control for vigilance tests [18]), as well as in more realistic task scenarios like flight [1,17,[19][20][21] or driving [1,22] simulation, using EEG signals. Typically in such studies, different levels of mental workload are induced by altering the difficulty of the task being performed or by introducing secondary tasks.…”

Section: Introductionmentioning

confidence: 99%

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

Massaeli,

Power

2024

J. Neural Eng.

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Objective. To date, most research on EEG-based mental workload detection for passive BCI applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used. Approach. Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single modality conditions (i.e., those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e., those requiring both visual and auditory processing) and no-task/baseline conditions (i.e., when the individual is not engaged in either visual or auditory processing). Main results. Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%. Significance. The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.

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Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

Cited by 33 publications

References 72 publications

Virtual and Reality: A Neurophysiological Pilot Study of the Sarcophagus of the Spouses

Virtual and Reality: A Neurophysiological Pilot Study of the Sarcophagus of the Spouses

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

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