Mobile EEG identifies the re-allocation of attention during real-world activity

Ladouce, Simon; Donaldson, David I.; Dudchenko, Paul A.; Ietswaart, Magdalena

doi:10.1038/s41598-019-51996-y

Cited by 117 publications

(129 citation statements)

References 44 publications

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“…As can be seen, for all modalities, except ECG and breathing, average AUC values were higher in the treadmill condition. For EEG, these findings corroborate those of Ladouce et al (2019).…”

Section: Comparing Classification Performance: Bike Vs Treadmillsupporting

confidence: 83%

“…Recent research has shown that a human's attention to targets is reduced when walking relative to when standing still, due to processing demands produced by visual and inertial stimulation (Ladouce et al, 2019). As such, varying MW prediction capability is hypothesized based on the physical activity equipment used.…”

Section: Comparing Classification Performance: Bike Vs Treadmillmentioning

confidence: 99%

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WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

et al. 2020

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Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

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“…As can be seen, for all modalities, except ECG and breathing, average AUC values were higher in the treadmill condition. For EEG, these findings corroborate those of Ladouce et al (2019).…”

Section: Comparing Classification Performance: Bike Vs Treadmillsupporting

confidence: 83%

Section: Comparing Classification Performance: Bike Vs Treadmillmentioning

confidence: 99%

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

et al. 2020

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“…While we were able to measure significant, and expected, differences between ERPs following standard and target tones (we were able to detect a P3 response following rare stimuli as shown in Figure 2B and Table 2) we found no significant P3 differences between our conditions. Previous mobile EEG studies in naturalistic environments have shown a P3 amplitude decrease during increased task load in walking (Ladouce et al, 2019) and cycling (Zink et al, 2016;Scanlon et at., 2019;Scanlon et al, 2020). We expected that the increase in difficulty in the non-preferred riding condition was going to be marked by a more negative voltage within the P3 time window.…”

Section: P3 and Mmn/n2b Resultsmentioning

confidence: 94%

EEG in Motion: Using an Oddball Task to Explore Motor Interference in Active Skateboarding

Robles

Kuziek

Wlasitz

et al. 2020

Preprint

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Total number of words in manuscript: 8155. Total number of words in abstract: 178 AbstractRecent advancements in portable technology have opened new avenues in the study of human cognition outside research laboratories. This flexibility in methodology has led to the publication of several Electroencephalography (EEG) studies recording brain responses in real world scenarios such as cycling and walking outside. In the present study, we wanted to test the classic oddball task event related potentials (ERPs) while participants moved around a running track using an electric skateboard. This novel approach allows for the study of attention in motion while virtually removing body movement. Using the auditory oddball paradigm, we were able to measure the P3 and MMN-N2b components elicited by this task. We also found that compared to resting state, alpha power is attenuated in frontal and parietal regions during skateboarding. We also tested for the effect of stance preference in terms of P3 and alpha magnitude and found no differences in for either of these. By replicating the findings of the classic oddball task under such a novel environment this study extends our knowledge of brain function in highly ecologically valid scenarios. Attention in motion 3

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“…This study helps to pave the way for a fuller understanding of auditory perception in everyday contexts. Our work adds to the growing number of studies that show the general feasibility of beyond-the-lab EEG recordings (e.g., Debener et al, 2012; Ladouce, Donaldson, Dudchenko, & Ietswaart, 2019; Scanlon et al, 2019; Wascher et al, 2016), but more work is required to use the richness of this methodology.…”

Section: Resultsmentioning

confidence: 99%

Mobile ear-EEG to study auditory attention in everyday life

Hölle

Meekes

Bleichner

2020

Preprint

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Most research investigating auditory perception is conducted in controlled laboratory settings, potentially restricting its generalizability to the complex acoustic environment outside the lab. The present study, in contrast, investigated auditory attention with long-term recordings (>6 h) beyond the lab using a fully mobile, smartphone-based ear-centered electroencephalography (EEG) setup with minimal restrictions for participants. Twelve participants completed iterations of two variants of an oddball task where they had to react to target tones and to ignore standard tones. A rapid variant of the task (tones every 2 seconds, 5 minutes total time) was performed seated and with full focus in the morning, around noon and in the afternoon under controlled conditions. A sporadic variant (tones every minute, 160 minutes total time) was performed once in the morning and once in the afternoon while participants followed their normal office day routine. EEG data, behavioural data, and movement data (with a gyroscope) were recorded and analyzed. The expected increased amplitude of the P3 component in response to the target tone was observed for both the rapid and the sporadic oddball. Miss rates were lower and reaction times were faster in the rapid oddball compared to the sporadic one. The movement data indicated that participants spent most of their office day at relative rest. Overall, this study demonstrated that it is feasible to study auditory perception in everyday life with long-term ear-EEG.

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Mobile EEG identifies the re-allocation of attention during real-world activity

Cited by 117 publications

References 44 publications

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

EEG in Motion: Using an Oddball Task to Explore Motor Interference in Active Skateboarding

Mobile ear-EEG to study auditory attention in everyday life

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