Electroencephalographic time-frequency patterns of braking and acceleration movement preparation in car driving simulation

The objective of the present work was to identify electroencephalographic (EEG) components in order to distinguish between braking and accelerating intention in simulated car driving. To do so, we collected high-density EEG data from thirty participants while they were driving in a car simulator. The EEG was separated into independent components that were clustered across participants according to their scalp map topographies. For each component, time-frequency activity related to braking and acceleration events was determined through wavelet analysis, and the cortical generators were estimated through minimum norm source localisation. Comparisons of the time-frequency patterns of power and phase activations revealed that theta power synchronisation distinguishes braking from acceleration events 800 ms before the action and that phase-locked activity increases for braking 800 ms before foot movement in the theta-alpha frequency range. In addition, source reconstruction showed that the dorso-mesial part of the premotor cortex plays a key role in preparation of foot movement. Overall, the results illustrate that dorso-mesial premotor areas are involved in movement preparation while driving, and that low-frequency EEG rhythms could be exploited to predict drivers' intention to brake or accelerate.

[1]  Tony W. Wilson,et al.  Functional specialization within the supplementary motor area: A fNIRS study of bimanual coordination , 2014, NeuroImage.

[2]  Thomas E. Nichols,et al.  Combining voxel intensity and cluster extent with permutation test framework , 2004, NeuroImage.

[3]  Henrik Walter,et al.  The neural correlates of driving , 2001, Neuroreport.

[4]  R. Oostenveld,et al.  Independent EEG Sources Are Dipolar , 2012, PloS one.

[5]  M. Frank,et al.  Frontal theta as a mechanism for cognitive control , 2014, Trends in Cognitive Sciences.

[6]  Vince D. Calhoun,et al.  A selective review of simulated driving studies: Combining naturalistic and hybrid paradigms, analysis approaches, and future directions , 2012, NeuroImage.

[7]  J. Kalaska,et al.  Neural mechanisms for interacting with a world full of action choices. , 2010, Annual review of neuroscience.

[8]  J. Pineda The functional significance of mu rhythms: Translating “seeing” and “hearing” into “doing” , 2005, Brain Research Reviews.

[9]  S.J. Nasuto,et al.  Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles , 2015, Journal of Neuroscience Methods.

[10]  J. Pekar,et al.  Erratum: Different activation dynamics in multiple neural systems during simulated driving (Human Brain Mapping (2002) 16 (158-167)) , 2002 .

[11]  Matthew J. C. Crump,et al.  In Support of a Distinction between Voluntary and Stimulus-Driven Control: A Review of the Literature on Proportion Congruent Effects , 2012, Front. Psychology.

[12]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[13]  Ricardo Chavarriaga,et al.  Action prediction based on anticipatory brain potentials during simulated driving , 2015, Journal of neural engineering.

[14]  Scott T. Grafton,et al.  Understanding Actions of Others: The Electrodynamics of the Left and Right Hemispheres. A High-Density EEG Neuroimaging Study , 2010, PloS one.

[15]  J. Schoffelen,et al.  Source connectivity analysis with MEG and EEG , 2009, Human brain mapping.

[16]  R Chavarriaga,et al.  EEG-based decoding of error-related brain activity in a real-world driving task , 2015, Journal of neural engineering.

[17]  R. Miall,et al.  Frontoparietal theta activity supports behavioral decisions in movement-target selection , 2012, Front. Hum. Neurosci..

[18]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[19]  J. Pernier,et al.  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human , 1996, The Journal of Neuroscience.

[20]  F. Horak,et al.  An fMRI-compatible force measurement system for the evaluation of the neural correlates of step initiation , 2017, Scientific Reports.

[21]  R. Andersen,et al.  Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits , 2009, Neuron.

[22]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[23]  S. Makeig,et al.  Mining event-related brain dynamics , 2004, Trends in Cognitive Sciences.

[24]  C. Kennard,et al.  Functional role of the supplementary and pre-supplementary motor areas , 2008, Nature Reviews Neuroscience.

[25]  G. Goldberg Supplementary motor area structure and function: Review and hypotheses , 1985, Behavioral and Brain Sciences.

[26]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  Silvia Daun-Gruhn,et al.  Movement-related phase locking in the delta–theta frequency band , 2016, NeuroImage.

[29]  C. Keysers,et al.  μ-Suppression during Action Observation and Execution Correlates with BOLD in Dorsal Premotor, Inferior Parietal, and SI Cortices , 2011, The Journal of Neuroscience.

[30]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[31]  Loredana Zollo,et al.  Brain activity preceding a 2D manual catching task , 2009, NeuroImage.

[32]  G. Pfurtscheller,et al.  Foot and hand area mu rhythms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[33]  J. Pekar,et al.  Different activation dynamics in multiple neural systems during simulated driving , 2002, Human brain mapping.

[34]  R. B. Reilly,et al.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.

[35]  Paul B. Johnson,et al.  Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. , 1997, Annual review of neuroscience.

[36]  G. Rizzolatti,et al.  The Cortical Motor System , 2001, Neuron.

[37]  Kyungmin Su,et al.  The PREP pipeline: standardized preprocessing for large-scale EEG analysis , 2015, Front. Neuroinform..

[38]  P. Roland Organization of motor control by the normal human brain. , 1984, Human neurobiology.

[39]  Dorothy V. M. Bishop,et al.  Journal of Neuroscience Methods , 2015 .

[40]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[41]  G. Rizzolatti,et al.  The Organization of the Frontal Motor Cortex. , 2000, News in physiological sciences : an international journal of physiology produced jointly by the International Union of Physiological Sciences and the American Physiological Society.

[42]  E. Maris,et al.  Theta oscillations locked to intended actions rhythmically modulate perception , 2017, eLife.

[43]  D. Tucker,et al.  EEG source localization: Sensor density and head surface coverage , 2015, Journal of Neuroscience Methods.

[44]  Robert Oostenveld,et al.  Competitive interactions in sensorimotor cortex: oscillations express separation between alternative movement targets. , 2014, Journal of neurophysiology.

[45]  Michael X. Cohen,et al.  A neural microcircuit for cognitive conflict detection and signaling , 2014, Trends in Neurosciences.

[46]  Isabelle Guyon,et al.  A Stability Based Method for Discovering Structure in Clustered Data , 2001, Pacific Symposium on Biocomputing.

[47]  Stefan Haufe,et al.  EEG potentials predict upcoming emergency brakings during simulated driving , 2011, Journal of neural engineering.

[48]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[49]  Eleanor A. Maguire,et al.  Neural substrates of driving behaviour , 2007, NeuroImage.

[50]  G. Rizzolatti,et al.  The Dynamics of Sensorimotor Cortical Oscillations during the Observation of Hand Movements: An EEG Study , 2012, PloS one.

[51]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[52]  Michael X. Cohen,et al.  Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. , 2013, Journal of neurophysiology.

[53]  Stefan Haufe,et al.  Detection of braking intention in diverse situations during simulated driving based on EEG feature combination , 2015, Journal of neural engineering.

[54]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[55]  Stefan Haufe,et al.  Electrophysiology-based detection of emergency braking intention in real-world driving , 2014, Journal of neural engineering.