, Understanding the distracted brain: Why driving while using hands-free phones is risky behavior, 2012.

K. Young, J. D. Lee, and M. A. Regan, Driver distraction: Theory, effects, and mitigation, 2008.

P. Jolicoeur, Restricted attentional capacity between sensory modalities, Psychon Bull Rev, vol.6, pp.87-92, 1999.

J. Duncan, S. Martens, and R. Ward, Restricted attentional capacity within but not between sensory modalities, Nature, vol.387, pp.808-818, 1997.

Y. Gazes, B. C. Rakitin, J. Steffener, C. Habeck, B. Butterfield et al., Performance degradation and altered cerebral activation during dual performance: Evidence for a bottom-up attentional system, Behavioural brain research, vol.210, pp.229-239, 2010.

J. D. Lee, Dynamics of Driver Distraction: The process of engaging and disengaging, Annals of Advances in Automotive Medicine, vol.58, pp.24-32, 2014.

T. Strobach, M. Wendt, and M. Janczyk, Editorial: Multitasking: Executive Functioning in Dual-Task and Task Switching Situations, Frontiers in psychology, vol.9, pp.108-108, 2018.

J. F. Ettwig and A. W. Bronkhorst, Attentional Switches and Dual-Task Interference, PLOS ONE, vol.10, 2015.

N. M. Yusoff, R. F. Ahmad, C. Guillet, A. S. Malik, N. M. Saad et al., Selection of Measurement Method for Detection of Driver Visual Cognitive Distraction: A Review, IEEE Access, vol.5, pp.22844-22854, 2017.

K. L. Young and P. M. Salmon, Examining the relationship between driver distraction and driving errors: A discussion of theory, studies and methods, Safety Science, vol.50, pp.165-174, 2012.

Y. Liang and J. D. Lee, Combining cognitive and visual distraction: Less than the sum of its parts, Accident Analysis and Prevention, vol.42, pp.881-890, 2010.

D. B. Kaber, Y. Liang, Y. Zhang, M. L. Rogers, and S. Gangakhedkar, Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior, Transportation Research Part F: Traffic Psychology and Behaviour, vol.15, pp.491-501, 2012.

Y. K. Wang, T. P. Jung, and C. T. Lin, EEG-Based Attention Tracking During Distracted Driving, IEEE Trans Neural Syst Rehabil Eng, vol.23, pp.1085-1094, 2015.

A. Sonnleitner, M. S. Treder, M. Simon, S. Willmann, A. Ewald et al., EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study, Accident Analysis & Prevention, vol.62, pp.110-118, 2014.

N. Dahal, D. N. Nandagopal, B. Cocks, R. Vijayalakshmi, N. Dasari et al., TVAR modeling of EEG to detect audio distraction during simulated driving, J Neural Eng, vol.11, p.36012, 2014.

H. Almahasneh, W. Chooi, N. Kamel, and A. S. Malik, Deep in thought while driving: An EEG study on drivers' cognitive distraction, Transportation Research Part F: Traffic Psychology and Behaviour, vol.26, pp.218-226, 2014.

C. Lin, S. Chen, T. Chiu, H. Lin, and L. Ko, Spatial and temporal EEG dynamics of dual-task driving performance, Journal of neuroengineering and rehabilitation, vol.8, pp.11-11, 2011.

M. K. Wali, M. Murugappan, and B. Ahmad, Subtractive Fuzzy Classifier Based Driver Distraction Levels Classification Using EEG, Journal of Physical Therapy Science, vol.25, pp.1055-1058, 2013.

H. Nakatani and C. Van-leeuwen, Individual differences in perceptual switching rates; the role of occipital alpha and frontal theta band activity, Biol Cybern, vol.93, pp.343-54, 2005.

H. Nakatani and C. Van-leeuwen, Transient synchrony of distant brain areas and perceptual switching in ambiguous figures, Biol Cybern, vol.94, pp.445-57, 2006.

D. Shimaoka, K. Kitajo, K. Kaneko, and Y. Yamaguchi, Transient process of cortical activity during Necker cube perception: from local clusters to global synchrony, Nonlinear Biomedical Physics, vol.4, pp.1-10, 2010.

H. Nakatani, N. Orlandi, and C. Van-leeuwen, Precisely timed oculomotor and parietal EEG activity in perceptual switching, Cognitive Neurodynamics, vol.5, pp.399-409, 2011.

B. Güntekin and E. Ba?ar, A new interpretation of P300 responses upon analysis of coherences, Cognitive Neurodynamics, vol.4, pp.107-118, 2010.

T. J. Ozaki, N. Sato, K. Kitajo, Y. Someya, K. Anami et al., Traveling EEG slow oscillation along the dorsal attention network initiates spontaneous perceptual switching, Cognitive neurodynamics, vol.6, pp.185-198, 2012.

C. Carney, D. Mcgehee, K. Harland, M. Weiss, and M. Raby, Using naturalistic driving data to assess the prevalence of environmental factors and driver behaviors in teen driver crashes, AAA Foundation for Traffic Safety, 2015.

, Distracted Driving, NHTSA, 2014.

N. H. Administration, Distracted Driving, Traffic safety facts 2015: Distracted Driving 2013.(Report DOT HS 812132), ed: NHTSA's National Center for Statistis and Analysis, 2013.

N. H. Administration, Distracted Driving, Traffic safety facts 2015: Distracted Driving 2012.(Report DOT HS 812012), ed: NHTSA's National Center for Statistics and Analysis, 2012.

E. H. Choi, Crash Factors in Intersection-Related Crashes: An On-Scene Perspective, 2010.

E. K. Miller and T. J. Buschman, Cortical circuits for the control of attention, Curr Opin Neurobiol, vol.23, pp.216-238, 2013.

M. M. Chun, J. D. Golomb, and N. B. Turk-browne, A Taxonomy of External and Internal Attention, Annual Review of Psychology, vol.62, pp.73-101, 2011.

E. B. Goldstein, Sensation and Perception, 2010.

M. N. Hebart and G. Hesselmann, What visual information is processed in the human dorsal stream?, The Journal of Neuroscience, vol.32, pp.8107-8109, 2012.

J. D. Lee, S. C. Roberts, J. D. Hoffman, and L. S. Angell, Scrolling and driving: how an MP3 player and its aftermarket controller affect driving performance and visual behavior, Hum Factors, vol.54, pp.250-63, 2012.

S. Fox and M. Hoffman, Escalation Behavior as a Specific Case of Goal-Directed Activity: A Persistence Paradigm, Basic and Applied Social Psychology, vol.24, pp.273-285, 2002.

F. Baluch and L. Itti, Mechanisms of top-down attention, Trends in Neurosciences, vol.34, pp.210-224, 2011.

S. , Task switching, Trends in Cognitive Sciences, vol.7, pp.134-140, 2003.

R. Alzahabi and M. W. Becker, The association between media multitasking, taskswitching, and dual-task performance, Journal of Experimental Psychology: Human Perception and Performance, vol.39, pp.1485-1495, 2013.

N. J. Cepeda, A. F. Kramer, and J. C. Gonzalez-de-sather, Changes in executive control across the life span: Examination of task-switching performance, Developmental Psychology, vol.37, pp.715-730, 2001.

G. Wylie and A. Allport, Task switching and the measurement of "switch costs, Psychol Res, vol.63, pp.212-245, 2000.

T. J. Buschman and E. K. Miller, Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices, Science, vol.315, pp.1860-1862, 2007.

A. T. Welford, Fundamentals of skill, 1968.

R. Beurskens, F. Steinberg, F. Antoniewicz, W. Wolff, and U. Granacher, Neural Correlates of Dual-Task Walking: Effects of Cognitive versus Motor Interference in Young Adults, Neural Plasticity, vol.2016, p.9, 2016.

C. E. Little and M. Woollacott, EEG measures reveal dual-task interference in postural performance in young adults, Exp Brain Res, vol.233, pp.27-37, 2015.

C. Lin, S. Chen, T. Chiu, H. Lin, and L. Ko, Spatial and temporal EEG dynamics of dual-task driving performance, Journal of neuroengineering and rehabilitation, vol.8, pp.11-11, 2011.

W. R. Thomas, A. Ranney, and M. J. Goodman, NHTSA Driver Distraction Research: Past Present and Future, 17th International Technical Conference on the Enhanced Safety of Vehicles

J. Engström and G. Markkula, Effects of visual and cognitive distraction on lane change test performance

L. Márquez, V. Cantillo, and J. Arellana, Mobile phone use while driving: A hybrid modeling approach, Accident Analysis & Prevention, vol.78, pp.73-80, 2015.

T. Dukic, C. Ahlstrom, C. Patten, C. Kettwich, and K. Kircher, Effects of electronic billboards on driver distraction, Traffic Inj Prev, vol.14, pp.469-76, 2013.

J. Engström, E. Johansson, and J. Östlund, Effects of visual and cognitive load in real and simulated motorway driving, Transportation Research Part F: Traffic Psychology and Behaviour, vol.8, pp.97-120, 2005.

A. Mack and I. Rock, Inattentional blindness, 1998.

M. R. Endsley, Toward a Theory of Situation Awareness in Dynamic Systems, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol.37, pp.32-64, 1995.

D. A. Sousa, How the brain learns, 2011.

S. E. Lee, S. G. Klauer, E. C. Olsen, B. G. Simons-morton, T. A. Dingus et al., Detection of Road Hazards by Novice Teen and Experienced Adult Drivers, Transportation research record, vol.2078, pp.26-32, 2008.

S. G. Klauer, F. Guo, B. G. Simons-morton, M. C. Ouimet, S. E. Lee et al., Distracted driving and risk of road crashes among novice and experienced drivers, N Engl J Med, vol.370, pp.54-63, 2014.

C. A. Bolstad, Situation awareness: does it change with age?, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp.272-276, 2001.

R. Caserta and L. Abrams, The relevance of situation awareness in older adults' cognitive functioning: a review, European Review of Aging and Physical Activity, vol.4, pp.3-13, 2007.

D. Kahneman, Attention and effort, 1973.

J. Miller and R. Ulrich, Bimanual response grouping in dual-task paradigms, Q J Exp Psychol (Hove), vol.61, pp.999-1019, 2008.

D. L. Strayer and W. A. Johnston, Driven to distraction: dual-Task studies of simulated driving and conversing on a cellular telephone, Psychol Sci, vol.12, pp.462-468, 2001.

P. Herath, T. Klingberg, J. Young, K. Amunts, and P. Roland, Neural correlates of dual task interference can be dissociated from those of divided attention: an fMRI study, Cereb Cortex, vol.11, pp.796-805, 2001.

S. W. Brown, Attentional resources in timing: interference effects in concurrent temporal and nontemporal working memory tasks, Percept Psychophys, vol.59, pp.1118-1158, 1997.

L. Karlin and R. Kestenbaum, Effects of number of alternatives on the psychological refractory period, Q J Exp Psychol, vol.20, pp.167-78, 1968.

D. Yanchao, H. Zhencheng, K. Uchimura, and N. Murayama, Driver Inattention Monitoring System for Intelligent Vehicles: A Review, IEEE Transactions on, vol.12, pp.596-614, 2011.

T. Horberry, J. Anderson, M. A. Regan, T. J. Triggs, and J. Brown, Driver distraction: The effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance, Accident Analysis & Prevention, vol.38, pp.185-191, 2006.

J. E. Törnros and A. K. Bolling, Mobile phone use-Effects of handheld and handsfree phones on driving performance, Accident Analysis & Prevention, vol.37, pp.902-909, 2005.

K. L. Young, P. M. Salmon, and M. Cornelissen, Distraction-induced driving error: An on-road examination of the errors made by distracted and undistracted drivers, Accident Analysis & Prevention, vol.58, pp.218-225, 2013.

J. K. Caird, C. R. Willness, P. Steel, and C. Scialfa, A meta-analysis of the effects of cell phones on driver performance, Accid Anal Prev, vol.40, pp.1282-93, 2008.

M. E. Rakauskas, L. J. Gugerty, and N. J. Ward, Effects of naturalistic cell phone conversations on driving performance, Journal of Safety Research, vol.35, pp.453-464, 2004.

M. A. Recarte and L. M. Nunes, Effects of verbal and spatial-imagery tasks on eye fixations while driving, Journal of Experimental Psychology: Applied, vol.6, p.31, 2000.

J. Pohl, W. Birk, and L. Westervall, A driver-distraction-based lane-keeping assistance system, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol.221, pp.541-552, 2007.

D. L. Strayer, J. M. Cooper, J. Turrill, J. Coleman, N. Medeiros-ward et al., Measuring cognitive distraction in the automobile, 2013.

C. J. Patten, A. Kircher, J. Östlund, and L. Nilsson, Using mobile telephones: cognitive workload and attention resource allocation, Accident analysis & prevention, vol.36, pp.341-350, 2004.

M. Chan and A. Singhal, The emotional side of cognitive distraction: Implications for road safety, Accident Analysis & Prevention, vol.50, pp.147-154, 2013.

K. Kircher, C. Ahlstrom, and A. Kircher, Comparison of two eye-gaze based realtime driver distraction detection algorithms in a small-scale field operational test, Proc. 5th Int. Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp.16-23, 2009.

T. Hirayama, K. Mase, and K. Takeda, Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction, International Journal of Vehicular Technology, vol.2013, issue.8, 2013.

Y. Yang, H. Sun, T. Liu, G. Huang, and O. Sourina, Driver Workload Detection in On-Road Driving Environment Using Machine Learning, Proceedings of ELM-2014, vol.2, pp.389-398, 2015.

C. Gabaude, B. Baracat, C. Jallais, M. Bonniaud, and A. Fort, Cognitive load measurement while driving, Human Factors: a view from an integrative perspective, pp.67-80, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01027475

J. Healey and R. W. Picard, Detecting stress during real-world driving tasks using physiological sensors, IEEE Transactions on, vol.6, pp.156-166, 2005.

M. K. Wali, M. Murugappan, and B. Ahmmad, Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG, Mathematical Problems in Engineering, vol.2013, p.10, 2013.

A. Cuenen, E. M. Jongen, T. Brijs, K. Brijs, M. Lutin et al., Does attention capacity moderate the effect of driver distraction in older drivers?, Accident Analysis & Prevention, vol.77, pp.12-20, 2015.

S. G. Hart and L. E. Staveland, Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research, Advances in psychology, vol.52, pp.139-183, 1988.

S. G. Hart, Nasa-Task Load Index (NASA-TLX); 20 Years Later, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol.50, pp.904-908, 2006.

H. Alm and L. Nilsson, The effects of a mobile telephone task on driver behaviour in a car following situation, Accident Analysis & Prevention, vol.27, pp.707-715, 1995.

K. L. Young, P. M. Salmon, and M. Cornelissen, Missing links? The effects of distraction on driver situation awareness, 2013.

K. Young, M. Lenne, V. Beanland, P. Salmon, and N. Stanton, Drivers' visual scanning and head check behavior on approach to urban rail level crossings, Advances in Human Aspects of Transportation Part 1, 2014.

D. I. Swedler, K. M. Pollack, and A. C. Gielen, Understanding commercial truck drivers' decision-makin process concerning distracted driving, Accident Analysis & Prevention, vol.78, pp.20-28, 2015.

Y. Liang and J. D. Lee, A hybrid Bayesian Network approach to detect driver cognitive distraction, Transportation research part C: emerging technologies, vol.38, pp.146-155, 2014.

G. Weller and B. Schlag, A robust method to detect driver distraction, European Conference on Human Centred Design for Intelligent Transport Systems, 2nd, 2010.

Y. Liang, M. L. Reyes, and J. D. Lee, Real-time detection of driver cognitive distraction using support vector machines, IEEE Transactions on, vol.8, pp.340-350, 2007.

M. Miyaji, H. Kawanaka, and K. Oguri, Driver's cognitive distraction detection using physiological features by the adaboost, Intelligent Transportation Systems, 2009. ITSC '09. 12th International IEEE Conference on, pp.1-6, 2009.

P. L. Nunez and R. Srinivasan, Electric fields of the brain: the neurophysics of EEG, 2006.

J. W. Britton, L. C. Frey, J. Hopp, P. Korb, M. Koubeissi et al., Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants, 2016.

P. Adjamian, The Application of Electro-and Magneto-Encephalography in Tinnitus Research -Methods and Interpretations, Frontiers in Neurology, vol.5, 2014.

M. F. Bear, B. Connors, and M. Paradiso, Neuroscience: Exploring the brain, Computational and Mathematical Methods in Medicine Gastroenterology Research and Practice Evidence-Based Complementary and Alternative Medicine, vol.2014, 2007.

J. A. Wilson and J. Richardson, Principles of animal physiology

A. Miyake, N. P. Friedman, M. J. Emerson, A. H. Witzki, A. Howerter et al., The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: A latent variable analysis, Cognitive psychology, vol.41, pp.49-100, 2000.

D. T. Stuss and M. P. Alexander, Executive functions and the frontal lobes: a conceptual view, Psychological research, vol.63, pp.289-298, 2000.

D. T. Stuss, Functions of the frontal lobes: relation to executive functions, Journal of the international neuropsychological Society, vol.17, pp.759-765, 2011.

S. Salenius and R. Hari, Synchronous cortical oscillatory activity during motor action, Current Opinion in Neurobiology, vol.13, pp.678-684, 2003.

B. Kolb and I. Q. Whishaw, Fundamentals of human neuropsychology, 2009.

E. R. Kandel, J. H. Schwartz, T. M. Jessell, D. O. Biochemistry, M. B. Jessell et al., Principles of neural science vol, vol.4, 2000.

S. L. Galetta, Occipital Lobe?, Reference Module in Neuroscience and Biobehavioral Psychology, 2017.

K. Tanaka, Temporal Lobe, International Encyclopedia of the Social & Behavioral Sciences, pp.15595-15599, 2001.

R. W. Homan, J. Herman, and P. Purdy, Cerebral location of international 10-20 system electrode placement, Electroencephalography and clinical neurophysiology, vol.66, pp.376-382, 1987.

B. Burle, L. Spieser, C. Roger, L. Casini, T. Hasbroucq et al., Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view, Int J Psychophysiol, vol.97, pp.210-230, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01309952

I. Feinberg, R. L. Koresko, and N. Heller, EEG sleep patterns as a function of normal and pathological aging in man, Journal of psychiatric research, vol.5, pp.107-144, 1967.

D. V. Cicchetti and T. Allison, A new procedure for assessing reliability of scoring EEG sleep recordings, American Journal of EEG Technology, vol.11, pp.101-110, 1971.

D. De-ridder, P. Manning, S. L. Leong, S. Ross, W. Sutherland et al., The brain, obesity and addiction: an EEG neuroimaging study, Scientific Reports, vol.6, p.34122, 2016.

H. F. Ieong and Z. Yuan, Resting-State Neuroimaging and Neuropsychological Findings in Opioid Use Disorder during Abstinence: A Review, Frontiers in Human Neuroscience, vol.11, 2017.

L. F. Tófoli and D. B. De-araujo, Chapter Seven -Treating Addiction: Perspectives from EEG and Imaging Studies on Psychedelics, International Review of Neurobiology, vol.129, pp.157-185, 2016.

P. Mitra, Observed brain dynamics, 2007.

N. Arunkumar, K. Ramkumar, S. Hema, A. Nithya, P. Prakash et al., Fuzzy Lyapunov exponent based onset detection of the epileptic seizures, 2013 IEEE Conference on Information & Communication Technologies, pp.701-706, 2013.

S. Khalighi, T. Sousa, G. Pires, and U. Nunes, Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels, Expert Systems with Applications, vol.40, pp.7046-7059, 2013.

D. P. Subha, P. K. Joseph, R. Acharya, and C. M. Lim, EEG signal analysis: a survey, Journal of medical systems, vol.34, pp.195-212, 2010.

S. Motamedi-fakhr, M. Moshrefi-torbati, M. Hill, C. M. Hill, and P. R. White, Signal processing techniques applied to human sleep EEG signals-A review, Biomedical Signal Processing and Control, vol.10, pp.21-33

A. S. Al-fahoum and A. A. Al-fraihat, Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains, ISRN Neuroscience, vol.2014, p.7, 2014.

D. H. Blackwood and W. J. Muir, Cognitive brain potentials and their application, Br J Psychiatry Suppl, pp.96-101, 1990.

S. Sur and V. K. Sinha, Event-related potential: An overview, Industrial psychiatry journal, vol.18, pp.70-73, 2009.

A. M. Beres, Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research, Applied Psychophysiology and Biofeedback, vol.42, pp.247-255, 2017.

S. J. Luck, An introduction to the event-related potential technique, 2014.

J. S. Richman and J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, American Journal of Physiology-Heart and Circulatory Physiology, vol.278, pp.2039-2049, 2000.

Q. Liu, L. Ma, S. Z. Fan, M. F. Abbod, and J. S. Shieh, Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries, PeerJ, vol.6, p.4817, 2018.

G. J. Jiang, S. Fan, M. F. Abbod, H. Huang, J. Lan et al., Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience, BioMed Research International, vol.2015, issue.8, 2015.

Y. Kumar, M. L. Dewal, and R. S. Anand, Features extraction of EEG signals using approximate and sample entropy, 2012 IEEE Students' Conference on Electrical, pp.1-5, 2012.

J. W. Cooley and J. W. Tukey, An algorithm for the machine calculation of complex Fourier series, Mathematics of computation, vol.19, pp.297-301, 1965.

D. H. Bailey and P. N. Swarztrauber, A fast method for the numerical evaluation of continuous Fourier and Laplace transforms, SIAM Journal on Scientific Computing, vol.15, pp.1105-1110, 1994.

M. Abo-zahhad, S. Ahmed, and S. N. Seha, A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals, International Journal of Intelligent Systems and Applications (IJISA), vol.07, pp.48-54, 2015.

M. Tudor, L. T. Car, and K. Tudor, Hans berger (1873-1941) -The history of electroencephalography, Acta medica Croatica : c?asopis Hravatske akademije medicinskih znanosti, vol.59, pp.307-320, 2005.

G. Pfurtscheller, F. H. Lopes-da, and S. , Event-related EEG/MEG synchronization and desynchronization: basic principles, Clin Neurophysiol, vol.110, pp.1842-57, 1999.

S. Makeig, Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones, Electroencephalography and Clinical Neurophysiology, vol.86, pp.283-293, 1993.

R. Srinivasan, W. R. Winter, J. Ding, and P. L. Nunez, EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics, Journal of neuroscience methods, vol.166, pp.41-52, 2007.

S. M. Bowyer, Coherence a measure of the brain networks: past and present, Neuropsychiatric Electrophysiology, vol.2, p.1, 2016.

L. Zhavoronkova, A. Zharikova, and O. Maksakova, Why Voluntary Postural Training Improves Recovery of Mental and Motor Functions in Patients with Traumatic Brain Injury?, Journal of Behavioral and Brain Science, p.11, 2013.

X. Li, D. Song, P. Zhang, Y. Zhang, Y. Hou et al., Exploring EEG Features in Cross-Subject Emotion Recognition, Frontiers in neuroscience, vol.12, pp.162-162, 2018.

H. Almahasneh, N. Kamel, N. Walter, and A. S. Malik, EEG-based brain functional connectivity during distracted driving, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp.274-277, 2015.

S. Wang, Y. Zhang, C. Wu, F. Darvas, and W. A. Chaovalitwongse, Online prediction of driver distraction based on brain activity patterns, IEEE Transactions on Intelligent Transportation Systems, vol.16, pp.136-150, 2014.

G. Bajwa, M. Fazeen, and R. Dantu, Detecting driver distraction using stimuliresponse EEG analysis, 2019.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene Selection for Cancer Classification using Support Vector Machines, Machine Learning, vol.46, pp.389-422, 2002.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification and scene analysis, vol.3, 1973.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, pp.273-324

L. Breiman, Random forests, Machine learning, vol.45, pp.5-32, 2001.

L. Breiman, J. Friedman, C. J. Stone, and R. Olshen, Classification and regression trees. Brooks, 1984.

R. P. Rodrigues, P. M. Silveira, and P. F. Ribeiro, A survey of techniques applied to non-stationary waveforms in electrical power systems, Proceedings of 14th International Conference on Harmonics and Quality of Power -ICHQP 2010, pp.1-8, 2010.

F. , D. Nocera, and F. Ferlazzo, Resampling approach to statistical inference: Bootstrapping from event-related potentials data, Behavior Research Methods, Instruments, & Computers, vol.32, pp.111-119, 2000.

J. R. Stroop, Studies of interference in serial verbal reactions, Journal of experimental psychology, vol.18, p.643, 1935.

B. Schack, A. C. Chen, S. Mescha, and H. Witte, Instantaneous EEG coherence analysis during the Stroop task, Clin Neurophysiol, vol.110, pp.1410-1436, 1999.

C. Nombela, M. Nombela, P. Castell, T. García, J. López-coronado et al., Alpha-Theta Effects Associated with Ageing during the Stroop Test, PLoS ONE, vol.9, p.95657, 2014.

C. M. Macleod and P. A. Macdonald, Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention, Trends in Cognitive Sciences, vol.4, pp.383-391, 2000.

Y. Levin and J. Tzelgov, Conflict components of the Stroop effect and their "control, Frontiers in Psychology, vol.5, 2014.

E. Dalrymple-alford and B. Budayr, Examination of some aspects of the Stroop color-word test, Perceptual and motor skills, vol.23, pp.1211-1214, 1966.

A. Mouraux and G. D. Iannetti, Across-trial averaging of event-related EEG responses and beyond, Magn Reson Imaging, vol.26, pp.1041-54, 2008.

S. Wu, G. Hitchman, J. Tan, Y. Zhao, D. Tang et al., The neural dynamic mechanisms of asymmetric switch costs in a combined Stroop-task-switching paradigm, Scientific Reports, vol.5, p.10240, 2015.

A. Delorme and S. Makeig, EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis, J Neurosci Methods, vol.134, pp.9-21, 2004.

P. J. Durka, J. Zygierewicz, H. Klekowicz, J. Ginter, and K. J. Blinowska, On the statistical significance of event-related EEG desynchronization and synchronization in the time-frequency plane, IEEE Transactions on Biomedical Engineering, vol.51, pp.1167-1175, 2004.

E. Maris and R. Oostenveld, Nonparametric statistical testing of EEG-and MEGdata, Journal of Neuroscience Methods, vol.164, pp.177-190, 2007.

L. Hu, Z. G. Zhang, and Y. Hu, A time-varying source connectivity approach to reveal human somatosensory information processing, NeuroImage, vol.62, pp.217-228, 2012.

N. Meiran, Reconfiguration of processing mode prior to task performance, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol.22, pp.1423-1442, 1996.

N. Meiran, Modeling cognitive control in task-switching, Psychol Res, vol.63, pp.234-283, 2000.

M. , Costs of a predictable switch between simple cognitive tasks following severe closed-head injury, Neuropsychology, vol.20, pp.675-684, 2006.

D. A. Allport, E. A. Styles, and S. Hsieh, Shifting intentional set: Exploring the dynamic control of tasks, Attention and performance 15: Conscious and nonconscious information processing, pp.421-452, 1994.

S. M. Ravizza and C. S. Carter, Shifting set about task switching: behavioral and neural evidence for distinct forms of cognitive flexibility, Neuropsychologia, vol.46, pp.2924-2935, 2008.

J. F. Ettwig and A. W. Bronkhorst, Attentional Switches and Dual-Task Interference, PLoS ONE, vol.10, p.118216, 2015.

M. Ergen, S. Saban, E. Kirmizi-alsan, A. Uslu, Y. Keskin-ergen et al., Time-frequency analysis of the event-related potentials associated with the Stroop test, International Journal of Psychophysiology, vol.94, pp.463-472, 2014.

B. Güntekin and E. Ba?ar, Review of evoked and event-related delta responses in the human brain, International Journal of Psychophysiology, vol.103, pp.43-52, 2016.

P. Sauseng, W. Klimesch, M. Schabus, and M. Doppelmayr, Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory, Int J Psychophysiol, vol.57, pp.97-103, 2005.

S. Itthipuripat, J. R. Wessel, and A. R. Aron, Frontal theta is a signature of successful working memory manipulation, Experimental brain research, vol.224, pp.255-262, 2013.

O. Jensen and C. D. Tesche, Frontal theta activity in humans increases with memory load in a working memory task, Eur J Neurosci, vol.15, pp.1395-1404, 2002.

W. Klimesch, M. Doppelmayr, T. Pachinger, and H. Russegger, Event-related desynchronization in the alpha band and the processing of semantic information, Brain Res Cogn Brain Res, vol.6, pp.83-94, 1997.

C. Babiloni, C. Percio, S. Lopez, G. D. Gennaro, P. P. Quarato et al., Frontal Functional Connectivity of Electrocorticographic Delta and Theta Rhythms during Action Execution Versus Action Observation in Humans, Frontiers in Behavioral Neuroscience, vol.11, 2017.

T. Harmony, The functional significance of delta oscillations in cognitive processing, Frontiers in integrative neuroscience, vol.7, p.83, 2013.

F. Merienne, W. E. Marsh, B. Aykent, and J. Martinez, Institut Image -Le2i, IEEE Virtual Reality, pp.1-2, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01206644

R. J. Boik, A priori tests in repeated measures designs: Effects of nonsphericity, Psychometrika, vol.46, pp.241-255, 1981.

D. Purves, G. J. Augustine, and E. , Types of Eye Movements and Their Functions, 2001.

A. Delorme and S. Makeig, EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis, Journal of neuroscience methods, vol.134, pp.9-21, 2004.

E. M. Kolasinski, Army research Inst for the behavioral and social sciences Alexandria VA1995

Y. Lerman, G. Sadovsky, E. Goldberg, R. Kedem, E. Peritz et al., Correlates of military tank simulator sickness, Aviation, Space, and Environmental Medicine, vol.64, pp.619-622, 1993.

R. S. Kennedy, N. E. Lane, K. S. Berbaum, and M. G. , Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness, The International Journal of Aviation Psychology, vol.3, pp.203-220, 1993.

S. Rubio, E. Díaz, J. Martín, and J. M. Puente, Evaluation of Subjective Mental Workload: A Comparison of SWAT, NASA-TLX, and Workload Profile Methods, vol.53, pp.61-86, 2004.

A. Helland, S. Lydersen, L. Lervåg, G. D. Jenssen, J. Mørland et al., Driving simulator sickness: Impact on driving performance, influence of blood alcohol concentration, and effect of repeated simulator exposures, Accident Analysis & Prevention, vol.94, pp.180-187

J. He, E. Becic, Y. Lee, and J. S. Mccarley, Mind wandering behind the wheel: performance and oculomotor correlates, Human factors, vol.53, pp.13-21, 2011.

B. Güntekin, D. D. Emek-sava?, P. Kurt, G. G. Yener, and E. Ba?ar, Beta oscillatory responses in healthy subjects and subjects with mild cognitive impairment, NeuroImage. Clinical, vol.3, pp.39-46, 2013.

F. M. Stoll, C. R. Wilson, M. C. Faraut, J. Vezoli, K. Knoblauch et al., The Effects of Cognitive Control and Time on Frontal Beta Oscillations, Cerebral Cortex, vol.26, pp.1715-1732, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02358071

S. Liu, C. Xu, Y. Zhang, J. Liu, B. Yu et al., Feature selection of gene expression data for Cancer classification using double RBF-kernels, BMC Bioinformatics, vol.19, p.396, 2018.

T. Shadbahr, Application of variations of non-linear CCA for feature selection in drug sensitivity prediction, 2019.

T. Liu, S. Liu, Z. Chen, and W. Ma, An evaluation on feature selection for text clustering, Proceedings of the 20th international conference on machine learning (ICML-03), pp.488-495, 2003.

G. Forman, An extensive empirical study of feature selection metrics for text classification, Journal of machine learning research, vol.3, pp.1289-1305, 2003.

N. Dessì, E. Pascariello, and B. Pes, A comparative analysis of biomarker selection techniques, BioMed research international, vol.2013, 2013.

H. Abusamra, A comparative study of feature selection and classification methods for gene expression data of glioma, Procedia Computer Science, vol.23, pp.5-14, 2013.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of machine learning research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, LIBLINEAR: A library for large linear classification, Journal of machine learning research, vol.9, pp.1871-1874, 2008.

S. Kosub, A note on the triangle inequality for the Jaccard distance, Pattern Recognition Letters, vol.120, pp.36-38

P. Jaccard, Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines, Bull Soc Vaudoise Sci Nat, vol.37, pp.241-272, 1901.

N. M. Yusoff, R. F. Ahmad, C. Guillet, A. S. Malik, N. M. Saad et al., Selection of Measurement Method for Detection of Driver Visual Cognitive Distraction: A Review, IEEE Access, vol.5, pp.22844-22854, 2017.

.. .. Etat,

. .. Approche-scientifique,

.. .. Distraction-visuelle,

, Il y a deux niveaux qui sont construits par deux nombres pour le niveau facile (c'est-à-dire : 2 + 3) et trois nombres pour le niveau difficile (c'est-à-dire : 5 -4 + 2). La distinction entre les niveaux est le temps nécessaire pour compléter l'équation. Plus de chiffres nécessitent plus d'informations stockées dans la mémoire de travail, l'utilisation de la capacité cognitive requiert donc plus de temps pour accomplir la tâche. L'équation est affichée sur un moniteur placé à droite du participant. La figure 5.4 montre l'emplacement de l'écran à côté du simulateur de conduite. Bien que la taille de l'écran soit relativement grande (17 pouces), la résolution de l'affichage des stimuli est limitée à 720x450, Les participants doivent résoudre une équation mathématique afin de décider du point de jonction à choisir lorsqu'ils atteignent une intersection. Les stimuli de distraction sont des équations mathématiques d'addition et/ou de soustraction randomisées

, Une fois que le participant a atteint un panneau, l'écran passe de l'affichage du signe GPS droit à celui du signe GPS avec question mathématique. À ce moment, un marqueur est pointé pour avertir le groupe d'acquisition de données que le participant a entré un délai de distraction

, La distance de la "période d'affichage" est fixée à 160 m avant d'atteindre une intersection. Un autre marqueur sera à nouveau signalé lorsque le participant aura passé le deuxième panneau. Au total, les participants doivent traverser 10 intersections avant la fin de l'expérience

. Vii, Écart-type du frein (N) de complexité (référence, facile et difficile)

, Les caractéristiques qui satisfont à l'hypothèse de normalité (p > 0,05) seront soumises à un test de Mauchly sur la sphéricité pour vérifier l'hypothèse d'homogénéité de la sphéricité, p.5

, Si les deux hypothèses sont satisfaites, une ANOVA à mesures répétées à sens unique sera effectuée pour déterminer s'il existe des différences statistiquement significatives entre les moyennes des 3 niveaux de complexité. Si l'hypothèse de sphéricité n'est pas respectée, le résultat de l'ANOVA à mesures répétées à sens unique sera interprété lorsque les ajustements selon Greenhouse-Geisser [174] ont été effectués sur les degrés de liberté pour la complexité

, Une ANOVA à mesure répétée à sens unique ou un test de Friedman ont été utilisés pour déterminer s'il y avait des différences significatives entre trois niveaux de complexité (référence, facile et difficile) pour 12 caractéristiques de mouvements oculaires sélectionnées. Des valeurs aberrantes ont été identifiées dans la moyenne et l'écart-type du point de fixation sur l'axe des y et ont été exclues de l'analyse. Des tests de Shapiro-Wilk ont été effectués sur ces données pour évaluer l'hypothèse de normalité (p > 0,05) et seul le groupe de référence de l'écart-type de la vitesse de pointe viole cette hypothèse

, ) = 2,00, p = 0,368. Les tests de Mauchly sur la sphéricité ont été effectués sur les autres caractéristiques et tous satisfont aux hypothèses (p > 0,05), d'où l'exécution d'une ANOVA à mesure répétée à sens unique. Les résultats ont montré qu'il y avait des différences statistiquement significatives entre les niveaux de complexité de l'emplacement moyen de la fixation sur l'axe des x (F (2, 12) = 5,258, Le test de Friedman sur l'écart type de la vitesse de pointe a montré qu'il n'y avait pas de différences significatives entre les trois niveaux de complexité, vol.2

, Le test t de l'échantillon par paires effectué sur toutes les paires a révélé qu'il y avait une différence statistiquement significative entre le niveau de référence et le niveau difficile (t (6) = 2,547, p < 0,044) ainsi que le niveau facile et difficile (t (6) = 2,632, p < 0,039) du point de fixation moyen sur l'axe des x. La paire de référence et facile de l'emplacement moyen de fixation sur l'axe des y a également montré une différence significative, t (6) = 2,803, p < 0,031. L'analyse de l'EEG a été réalisée à l'aide de MATLAB® version R2018b (The MathWorks Inc., USA). Les données EEG acquises ont été déduites pour soustraire les tendances linéaires, puis filtrées en passe-bande entre 0,1 Hz et 50 Hz. Les données de la période de distraction, de la période de conduite de référence et de la période d'ouverture des yeux (pour la ligne de base) ont été extraites, Une analyse post hoc a été effectuée pour déterminer les niveaux statistiquement uniques parmi les paires en utilisant le test t de l'échantillon par paires. Les différences de moyenne ont été calculées entre trois paires : référence vs facile, référence vs difficile et facile vs difficile. Les valeurs aberrantes identifiées dans la paire

, Figure 6.2. Étapes pour extraire la valeur de cohérence de l'ensemble de données

, Au total, 150 caractéristiques sont utilisées dans l'exploration des caractéristiques pertinentes pour discriminer la distraction cognitive visuelle de la conduite à l'aide d'un signal EEG

, La méthode de sélection de caractéristiques est l'un des outils importants de l'exploration de données qui a montré ses capacités dans de nombreuses applications telles que la sélection de gènes dans la classification des cancers

S. Le, RFE a été mis en oeuvre pour la première fois par Guyon [140] dans l'application de la sélection de gènes pour la classification des cancers. Dans l'article de Guyon, un MVC linéaire a été utilisé pour produire un classement des caractéristiques en utilisant son poids. Les données d'entraînement sont utilisées pour construire une fonction discriminante, D(x), dans laquelle x est le vecteur d'entrée et les résultats prévus sont classés en fonction de la limite de décision apprise