From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels

Figalová, Nikol; Bieg, Hans-Joachim; Reiser, Julian Elias; Liu, Yuan-Cheng; Baumann, Martin; Chuang, Lewis; Pollatos, Olga

doi:10.1016/j.ijhcs.2023.103169

Cited by 6 publications

(4 citation statements)

References 60 publications

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“…We suppose they made this choice as the paradigm did not require any active perception of the auditory cues. However, as multiple studies showed that the P300 is relevant in passive oddball paradigms [31,33], we decided it was relevant to study the P300, and our results show significant differences in P300 amplitudes despite the passive paradigm. Additionally, with our experimental design, we are able to explore the relationship between questionnaire scores and neurophysiological measurements.…”

Section: Discussionmentioning

confidence: 87%

“…In active experiments, participants are asked to focus on the oddball stimulus and react to them, traditionally by counting or signaling the occurrence of a deviant stimulus [29,30]. In passive oddball paradigm, participants are asked to complete an unrelated task while being subjected to oddball stimuli [31][32][33][34]. In contrast to active oddball, participants are not asked to react or be attentive to these stimuli.…”

Section: Oddball Paradigm For the Measurement Of Presence 231 The Odd...mentioning

confidence: 99%

“…In passive oddball paradigm, studies mostly focus on the MMN as it reflects the brain's ability to automatically perform comparisons between stimulus. However, due to its high correlation with attention [23], and its presence in passive oddball paradigm [31,33], the P300 is a worthwhile ERP component to study in this context.…”

Section: Oddball Paradigm For the Measurement Of Presence 231 The Odd...mentioning

confidence: 99%

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Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

Savalle,

Pillette,

Won

et al. 2024

J. Neural Eng.

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Objective. Presence is an important aspect of user experience in virtual reality. It corresponds to the illusion of being physically located in a virtual environment. This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in virtual reality. Approach. In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence (BiP), this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical virtual environment. Main results. Through analyzing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e., the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions. Significance. Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the virtual environment.

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Section: Discussionmentioning

confidence: 87%

Section: Oddball Paradigm For the Measurement Of Presence 231 The Odd...mentioning

confidence: 99%

Section: Oddball Paradigm For the Measurement Of Presence 231 The Odd...mentioning

confidence: 99%

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Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

Savalle,

Pillette,

Won

et al. 2024

J. Neural Eng.

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“…A competing hypothesis is that, instead of resulting in just a broader scanning of the road environment, operating vehicle automation will instead increase the processing of driving-unrelated, potentially distracting events in the driving scene. Recent research on the effect of vehicle automation on drivers’ workload posit that the reduction in drivers’ responsibilities from manual to partially-automated driving–whereby the state of the human driver transitions from system operator to system supervisor (Cummings et al, 2013 )–will lower cognitive workload (Mishler & Chen, 2023 ; Solis-Marcos et al, 2017 ; Figalová et al, 2024 ). With a lowering of cognitive load, it is hypothesized that drivers may become more susceptible to potentially distracting events in the driving scene.…”

Section: Introductionmentioning

confidence: 99%

On investigating drivers’ attention allocation during partially-automated driving

Eddine,

Mulatti,

Biondi

2024

Cogn. Research

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The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers’ ability to supervise the automated system and resume manual control of the vehicle.

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A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

Qu,

Bu,

Zhao

et al. 2024

Advanced Engineering Informatics

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From Driver to Supervisor: Comparing Cognitive Load and EEG-Based Attentional Resource Allocation Across Automation Levels

Cited by 6 publications

References 60 publications

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

On investigating drivers’ attention allocation during partially-automated driving

A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

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