Skip to main content
Log in

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

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

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 (p-value = 0.05) than the subjects who do not have such connections.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. torcs.sourceforge.net/.

References

  • Al-Shargie F, Tariq U, Hassanin O, Mir H, Babiloni F, Al-Nashash H (2019) Brain connectivity analysis under semantic vigilance and enhanced mental states. Brain Sci 9(12):363

    Article  Google Scholar 

  • Ashburner J (2014) SPM12 manual

  • Bi L, Wang H, Teng T, Guan C (2018) A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information. IEEE Trans Neural Syst Rehabil Eng 26(10):1926–1934

    Article  Google Scholar 

  • Daunizeau J, David O, Stephan KE (2011) Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage 58(2):312–322

    Article  CAS  Google Scholar 

  • David O, Kiebel SJ, Harrison LM, Mattout J, Kilner JM, Friston KJ (2006) Dynamic causal modeling of evoked responses in EEG and MEG. Neuroimage 30(4):1255–1272

    Article  Google Scholar 

  • Del Vecchio A, Ubeda A, Sartori M, Azorin JM, Felici F, Farina D (2018) Central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force. J Appl Physiol 125(5):1404–1410

    Article  Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  Google Scholar 

  • Ernst M et al (2002) Decision-making in a risk-taking task: a PET study. Neuropsychopharmacology 26(5):682–691

    Article  Google Scholar 

  • Filimon F, Philiastides MG, Nelson JD, Kloosterman NA, Heekeren HR (2013) How embodied is perceptual decision making? Evidence for separate processing of perceptual and motor decisions. J Neurosci 33(5):2121–2136

    Article  CAS  Google Scholar 

  • Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19(4):1273–1302

    Article  CAS  Google Scholar 

  • Friston K et al (2008) Multiple sparse priors for the M/EEG inverse problem. Neuroimage 39(3):1104–1120

    Article  Google Scholar 

  • Friston K, Moran R, Seth AK (2013) Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol 23(2):172–178

    Article  CAS  Google Scholar 

  • Friston KJ (2016) Dynamic causal modeling of brain responses. fMRI Techniques and Protocols, pp. 241–264

  • Grill-Spector K (2003) The neural basis of object perception. Curr Opin Neurobiol 13(2):159–166

    Article  CAS  Google Scholar 

  • Haufe S et al (2014) Electrophysiology-based detection of emergency braking intention in real-world driving. J Neural Eng 11(5):056011

    Article  Google Scholar 

  • Haufe S, Treder MS, Gugler MF, Sagebaum M, Curio G, Blankertz B (2011) EEG potentials predict upcoming emergency brakings during simulated driving. J Neural Eng 8(5):056001

    Article  Google Scholar 

  • Hernández LG, Mozos OM, Ferrández JM, Antelis JM (2018) EEG-based detection of braking intention under different car driving conditions. Front Neuroinform 12:29

    Article  Google Scholar 

  • Jansen BH, Rit VG (1995) Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern 73(4):357–366

    Article  CAS  Google Scholar 

  • Kiebel SJ, Garrido MI, Moran RJ, Friston KJ (2008) Dynamic causal modelling for EEG and MEG. Cogn Neurodyn 2(2):121

    Article  Google Scholar 

  • Kim IH, Kim JW, Haufe S, Lee SW (2013) Detection of multi-class emergency situations during simulated driving from ERP. In: Brain-Computer Interface (BCI), 2013 international winter workshop on, 2013, pp. 49–51: IEEE

  • Lee M, Yoon J-G, Lee S-W (2020) Predicting motor imagery performance from resting-state EEG using dynamic causal modeling. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2020.00321

    Article  PubMed  PubMed Central  Google Scholar 

  • Light GA et al (2010) Electroencephalography (EEG) and event-related potentials (ERPs) with human participants. Curr Protocols Neuroscience. https://doi.org/10.1002/0471142301.ns0625s52

    Article  Google Scholar 

  • Litvak V, Friston K (2008) Electromagnetic source reconstruction for group studies. Neuroimage 42(4):1490–1498

    Article  Google Scholar 

  • Michel CM, Brunet D (2019) EEG source imaging: a practical review of the analysis steps. Front Neurol 10:325

    Article  Google Scholar 

  • Miyakoshi M (2018) Makoto's preprocessing pipeline

  • Nguyen T-H, Chung W-Y (2019) Detection of driver braking intention using EEG signals during simulated driving. Sensors 19(13):2863

    Article  Google Scholar 

  • Penny WD, Stephan KE, Mechelli A, Friston KJ (2004) Comparing dynamic causal models. Neuroimage 22(3):1157–1172

    Article  CAS  Google Scholar 

  • Sharbrough F (1991) American electroencephalographic society guidelines for standard electrode position nomenclature. J Clin Neurophysiol 8:200–202

    Article  Google Scholar 

  • Starcke K, Brand M (2012) Decision making under stress: a selective review. Neurosci Biobehav Rev 36(4):1228–1248

    Article  Google Scholar 

  • Teng T, Bi L, Liu Y (2017) EEG-based detection of driver emergency braking intention for brain-controlled vehicles. IEEE Trans Intell Transp Syst 19(6):1766–1773

    Article  Google Scholar 

  • Van Essen DC, Anderson CH, Felleman DJ (1992) Information processing in the primate visual system: an integrated systems perspective. Science 255(5043):419–423

    Article  Google Scholar 

  • Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics.". PloS one 8(7):e68910

    Article  CAS  Google Scholar 

  • Yang M, Xing P, Flynn T, Tsuge B, Lawrence J, Siegmund GP (2018) The effect of target features on Toyota’s autonomous emergency braking system. SAE Technical Paper 0148–7191

Download references

Acknowledgements

We would like to thank the neurotechnology group of Thechnische Universität Berlin, Stefan Haufe, Matthias Straders, Manfred F Gugler, Max Sagebaum, Gabriel Curio and Benjamin Blankertz, for allowing us to work on the data which is available on http://bnci-horizon-2020.eu/ website.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Motie Nasrabadi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The experiment was conducted in accordance with the Declaration of Helsinki and written informed consent was given by all participants.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabahi, Y., Setarehdan, S.K. & Nasrabadi, A.M. Dynamic causal modeling of evoked responses during emergency braking: an ERP study. Cogn Neurodyn 16, 353–363 (2022). https://doi.org/10.1007/s11571-021-09716-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-021-09716-8

Keywords

Navigation