Evidence map›Paper›PMID 35401862›Full record

ArticleCognitive neurodynamics2022

Dynamic causal modeling of evoked responses during emergency braking: an ERP study.

Yasaman Sabahi, Seyed Kamaledin Setarehdan, Ali Motie Nasrabadi

Abstract read
In one paragraph

Article in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yasaman SabahiDepartment of Biomedical Engineering-Bioelectric, Faculty of Medical Science and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Seyed Kamaledin SetarehdanControl and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Ali Motie NasrabadiDepartment of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.ORCID 0000-0002-3702-8547

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (

Indexed as

Driving simulationDynamic causal modelling (DCM)Effective connectivityEmergency brakingEvent related potential signals (ERP)

Identifiers

PMID35401862
PMCPMC8934904

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.