Literature DB >> 25111850

Electrophysiology-based detection of emergency braking intention in real-world driving.

Stefan Haufe1, Jeong-Woo Kim, Il-Hwa Kim, Andreas Sonnleitner, Michael Schrauf, Gabriel Curio, Benjamin Blankertz.   

Abstract

OBJECTIVE: The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. APPROACH: Here, we replicate that experimental paradigm in a real car on a non-public test track. MAIN
RESULTS: Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. SIGNIFICANCE: Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).

Entities:  

Mesh:

Year:  2014        PMID: 25111850     DOI: 10.1088/1741-2560/11/5/056011

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  10 in total

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

Authors:  Yasaman Sabahi; Seyed Kamaledin Setarehdan; Ali Motie Nasrabadi
Journal:  Cogn Neurodyn       Date:  2021-09-16       Impact factor: 5.082

2.  Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals.

Authors:  Manuel J A Eugster; Tuukka Ruotsalo; Michiel M Spapé; Oswald Barral; Niklas Ravaja; Giulio Jacucci; Samuel Kaski
Journal:  Sci Rep       Date:  2016-12-08       Impact factor: 4.379

Review 3.  The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.

Authors:  Benjamin Blankertz; Laura Acqualagna; Sven Dähne; Stefan Haufe; Matthias Schultze-Kraft; Irene Sturm; Marija Ušćumlic; Markus A Wenzel; Gabriel Curio; Klaus-Robert Müller
Journal:  Front Neurosci       Date:  2016-11-21       Impact factor: 4.677

4.  EEG-Based Detection of Braking Intention Under Different Car Driving Conditions.

Authors:  Luis G Hernández; Oscar Martinez Mozos; José M Ferrández; Javier M Antelis
Journal:  Front Neuroinform       Date:  2018-05-29       Impact factor: 4.081

5.  Investigating Established EEG Parameter During Real-World Driving.

Authors:  Janna Protzak; Klaus Gramann
Journal:  Front Psychol       Date:  2018-11-23

6.  Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies.

Authors:  Luca Ascari; Anna Marchenkova; Andrea Bellotti; Stefano Lai; Lucia Moro; Konstantin Koshmak; Alice Mantoan; Michele Barsotti; Raffaello Brondi; Giovanni Avveduto; Davide Sechi; Alberto Compagno; Pietro Avanzini; Jonas Ambeck-Madsen; Giovanni Vecchiato
Journal:  Sensors (Basel)       Date:  2021-12-07       Impact factor: 3.576

7.  Deep multi-modal learning for joint linear representation of nonlinear dynamical systems.

Authors:  Shaodi Qian; Chun-An Chou; Jr-Shin Li
Journal:  Sci Rep       Date:  2022-07-27       Impact factor: 4.996

8.  EEG-EMG coupling as a hybrid method for steering detection in car driving settings.

Authors:  Giovanni Vecchiato; Maria Del Vecchio; Jonas Ambeck-Madsen; Luca Ascari; Pietro Avanzini
Journal:  Cogn Neurodyn       Date:  2022-01-11       Impact factor: 3.473

9.  The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.

Authors:  Daniel E Callan; Cengiz Terzibas; Daniel B Cassel; Masa-Aki Sato; Raja Parasuraman
Journal:  Front Hum Neurosci       Date:  2016-04-27       Impact factor: 3.169

10.  BCI to Potentially Enhance Streaming Images to a VR Headset by Predicting Head Rotation.

Authors:  Anne-Marie Brouwer; Jasper van der Waa; Hans Stokking
Journal:  Front Hum Neurosci       Date:  2018-10-16       Impact factor: 3.169

  10 in total

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