In this thesis, we develop quantitative techniques that combine independent component analysis, temporal matching filter, time frequency spectrum analysis, correlation analysis, adaptive feature selection mechanism, and fuzzy neural network models for ongoing assessments of the transient event-related brain dynamics and the level of alertness of drivers by investigating the neurobiological mechanisms underlying non-invasively recorded multidimensional electroencephalographic (EEG) brain dynamics in the virtual-reality-based cognitive driving tasks. We then apply these methodologies to the issue of driving safety and focus on two most frequent happened events on the roads, the visual traffic-light detection task and the continuous lane-keeping driving task in order to maintain the subject’s maximum driving performance for preventing the traffic accidents and extend the applications of brain research to general populations (not limited to lock-in patients). In this thesis, we have made significant progresses in several aspects. (1) The use of virtual-reality technology not only allows subjects to interact directly with virtual objects, but also provides a well-controlled realistic experimental environment to avoid the risk of operating on the real world. (2) The computational approaches are capable of providing high spatial and temporal resolutions by using multidimensional EEG information obtained from an array of scalp electrodes and to model the dynamics of the underlying brain networks. (3) Comparing to the traditional time-domain overlap-added averaged methods, the introduced ICA algorithm can analyze brain activities in single trials correctly without first averaging on trials. The temporal independence and spatial stability make ICA to effectively remove non-brain artifacts to
classification. (5) We also use the moving-average time-frequency spectral analysis to avoid confounds caused by miscancellation of positive and negative potentials from different sources to the recording electrodes, and characterize the perturbations in the oscillatory dynamics of ongoing EEG. (6) The use of the correlation analysis can provide objective measurement of driver’s cognitive state and is helpful to extract effective features. (7) For online applications, we proposed a novel adaptive feature selection mechanism combined with the Self-cOnstructing Neuro-Fuzzy Inference Network model to accuracy identify the transient brain response to different visual stimuli and estimate/predict the actual driving performance of individual subjects.
Experimental results demonstrated that the proposed ICA-based methods can achieve a high recognition rate on average up to 85% in classifications of the brain cognitive responses related to visual traffic-light detection task. These high-accuracy results can be further transformed as the control/monitoring signals of on-line brain computer interfaces in the driving-safety systems. Another proposed EEG-based technology for drowsiness estimation also showed that it is feasible to accurately estimate individual driving performance accompanying loss of alertness. In this study, we demonstrated a close relationship between minute-scale changes in driving performance and the EEG power spectrum. This relationship appears stable within individuals across sessions, but is somewhat variable between subjects.
We also propose four strategies to explore the optimal and economic way to select effective EEG-based features. The first approach uses power spectrum of only 2 EEG channels, where once an estimator has been developed for each driver, based on limited pilot testing, the method uses only spontaneous EEG signals from the individual, and does not require further collection or analysis of operator performance. Another approach apply the ICA algorithm in the training session to locate the optimal position to wire EEG electrodes and uses 2 EEG channels located at central electrodes of the selected ICA components. Averaged accuracies
for 5 subjects achieve 79% using fuzzy neural network model. This method dramatically increases the accuracy than the first one and does not require collecting more EEG channels data in testing session. The third approach directly uses 10 log bandpower of 2 optimal ICA components as input features and achieves the maximum averaged accuracies to 84.8%. For the purpose of the online application, we proposed an automatic feature selecting mechanism combined with SONFIN to estimating driving performance and the averaged accuracies for five subjects can achieve high to 87%. Although the accuracy using adaptive feature selection mechanism is lower than those selected manually, the computational methods we employed in this study were well within the capabilities of modern portable embedded digital signal processing hardware to perform in real time alertness monitoring system.
Bibliography
[1] A. F. Kramer and D. L. Strayer, “Assessing the development of automatic processing:
An application of dual-task and event-related brain potential methodologies,”
Biological Psychology, Vol. 26, pp. 231-267, 1998.
[2] G. F. Wilson and F. Fisher, “Cognitive task classification based upon topographic EEG data,” Biological Psychology, Vol. 40, pp. 239-50, 1995.
[3] G. Pfurtscheller and A. Aranibar, “Event-related cortical desynchronization detected by power measurements of scalp EEG,” Electroencephalography and Clinical Neurophysiology, Vol. 42, pp. 817-826, 1977.
[4] G. Pfurtscheller and C. Andrew, “Event-Related changes of band power and coherence:
methodology and interpretation,” Journal of Clinical Neurophysiology, Vol. 16, pp.
512-519, 1999.
[5] S. J. Williamson, L. Kaufman, S. Curtis, Z. L. Lu, C. M. Michel, and J. Z. Wang,
“Neural substrates of working memories are revealed magnetically by the local suppression of alpha rhythm,” Electroencephalography and Clinical Neurophysiology.
Supplement, Vol. 47, pp. 163-80, 1996.
[6] P. Sykacek, S. J. Roberts, and M. Stokes, "Adaptive BCI based on variational Bayesian Kalman filtering: An empirical evaluation," IEEE Transactions on Biomedical Engineering, Vol. 51, pp. 719-727, May, 2004.
[7] D. J. McFarland, G. W. Neat, R. F. Read, and J. R. Wolpaw, “An EEG-based method for graded cursor control,” Psychobiology, Vol. 21, No. 1, pp. 77-81, 1993.
[8] G. Pfurtscheller, D. Flotzinger, M. Pregenzer, J. Wolpaw, and D. McFarland,
“EEG-based Brain Computer Interface (BCI),” Medical Progress through Technology, Vol. 21, pp. 111-121, 1996.
[9] T. M. Vaughan, J. R. Wolpaw, and E. Donchin, “EEG-based communication: prospects and problems," IEEE Trans. on Rehabilitation Engineering, Vol. 4, No. 4, pp. 425-430, 1996.
[10] L. A. Farwell and E. Donchin, “Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials,” Electroenceph. Clin. Neurophysiol., pp. 510-523, 1988.
[11] C. E. Davila and R. Srebro, "Subspace averaging of steady-state visual evoked potentials," IEEE Transactions on Biomedical Engineering, Vol. 47, pp. 720-728, Jun.
2000.
[12] T. J. Dasey and E. Micheli-Tzanakou, "Detection of multiple sclerosis with visual evoked potentials - an unsupervised computational intelligence system," IEEE Transactions on Information Technology in Biomedicine, Vol. 4, pp. 216-224, Sep.
2000.
[13] C. E. Davila, A. Abaye, and A. Khotanzad, "Estimation of single sweep steady-state visual evoked potentials by adaptive line enhancement," IEEE Transactions on Biomedical Engineering, Vol. 41, pp. 197-200, Feb. 1994.
[14] X. R. Gao, D. F. Xu, M. Cheng, and S. K. Gao, "A BCI-based environmental controller for the motion-disabled," IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, pp. 137-140, Jun. 2003.
[15] J. Klopp, K. Marinkovic, P. Chauvel, V. Nenov, and E. Halgren, “Early widespread cortical distribution of coherent fusiform face selective activity,” Human Brain Mapping, Vol. 11, pp. 286-293, 2000.
[16] E. Basar, EEG-brain dynamics: relation between EEG and brain evoked potentials, Elsevier/North-Holland Biomedical Press, New York, 1980.
[17] R. Barry, S. Kirkaikul, and D. Hodder, “EEG alpha activity and the ERP to target stimuli in an auditory oddball paradigm,” International Journal of Psychophysiology, Vol. 39, No.1, pp. 39-50, 2000.
[18] A. von Stein, C. Chiang, and P. Konig, “Top-down processing mediated by interareal synchronization,” Proceedings of the National Academy of Sciences, USA, Vol. 97, No.
26, pp. 14748-14753, 2000.
[19] S. Thorpe, D. Fize, and C. Marlot, “Speed of processing in the human visual system,”
Nature, Vol. 381, No. 6582, pp. 520-2, 1996.
[20] H. Ueno, M., Kaneda, and M. Tsukino,” Development of drowsiness detection system,”
Proceedings of the 1994 Vehicle Navigation and Information Systems Conference, Vol.
31, pp. 15–20, Sep. 1994.
[21] G. Hamouda, and F. F. Saccomanno, “Neural network model for truck driver fatigue accident detection,” Proceedings of the 1995 Electrical and Computer Engineering
Carnegie Mellon TruckSim: a tool to improve driving safety,” Proceedings of the 1998 IEEE 17th Conference on Digital Avionics Systems, Vol. 2, pp. I35/1 - I35/831, Nov.
1998.
[24] R. Grace, V. E. Byrne, D. M. Bierman, J. M. Legrand, D. Gricourt, B. K. Davis, J. J.
Staszewski, and B. Carnahan, ” A drowsy driver detection system for heavy vehicles”
Proceedings of the 1998 the 17th AIAA/IEEE/SAE Conference on Digital Avionics Systems, Vol. 2, pp. I36/1-I36/8, Nov. 1998.
[25] C. A. Perez, A. Palma, C. A. Holzmann, and C. Pena, ”Face and eye tracking algorithm based on digital image processing,” Proceedings of the 2001 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 2, pp.1178-1183, Oct. 2001.
[26] T. Pilutti, and G. Ulsoy, “Identification of driver state for lane-keeping tasks,” IEEE Trans. Syst, Man, Cybern., Part A: Systems and Humans, Vol. 29, pp. 486-502, Sep.
1999.
[27] J. C. Popieul, P. Simon, P. Loslever, “Using driver's head movements evolution as a drowsiness indicator,” Proceedings of the 2003 IEEE International Intelligent Vehicles Symposium, pp. 616–621, Jun. 2003.
[28] J. Qiang, Z. Zhiwei, P. Lan, “Real-time nonintrusive monitoring and prediction of driver fatigue,” IEEE Transactions on Vehicular Technology, Vol. 53, Issue: 4, pp.
1052– 068, Jul. 2004.
[29] K. Van Orden, W. Limbert, S. Makeig, and T. P. Jung, “Eye Activity correlates of workload during a visualspatial memory task,” Human Factors, Vol. 43, No. 1, pp.
111-21, 2001.
[30] R. S. Huang, C. J. Kuo, L. L. Tsai, and O. T. C. Chen, ”EEG pattern recognition-arousal states detection and classification,” Proceedings of the 1996 IEEE Conference on Neural Networks, Vol. 2, pp. 641-646, Jun. 1996.
[31] A. Vuckovic, V. Radivojevic, A. C. N. Chen, and D. Popovic, “Automatic recognition of alertness and drowsiness from EEG by an artificial neural network,” Medical Engineering and Physics Vol. 24, pp. 349-360, Jun. 2002.
[32] S. Roberts, I. Rezek, R. Everson, H. Stone, S. Wilson, and C. Alford, ”Automated assessment of vigilance using committees of radial basis function analysers,” IEE Science Measurement and Technology, Vol. 147, pp. 333–338, Nov. 2000.
[33] K. B. Khalifa, M. H. Bedoui, R. Raytchev, and M. Dogui, ”A portable device for alertness detection,” Proceedings of 2000 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology, pp. 584–586, Oct.
2000.
[34] B. J. Wilson, and T. D. Bracewell, ”Alertness monitor using neural networks for EEG analysis,” Proceedings of the 2000 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing X, Vol. 2, pp. 814-820, Dec. 2000.
[35] J. Santamaria and K. Chiappa, “The EEG of drowsiness in normal adults,” J. Clin.
Neurophysiol., Vol. 4, No. 4, pp. 327-382, 1987.
[36] Rechtschaffen, A., Kales, A. (Eds.): “A manual of standardized terminology in techniques and scoring system for sleep stages of human subjects,” BIS/BRI, UCLA, Los Angeles, CA, 1968.
[37] R. B. Berry, (Series Editors:) S. A. Sahn, and J. E. Heffner, “Sleep medicine pearls”, Hanley and Belfus Inc., Philadelphia, 1999.
[38] P. Parikh, and E. Micheli-Tzanakou, “Detecting drowsiness while driving using wavelet transform” Proceedings of the IEEE 30th Annual Northeast on Bioengineering Conference, pp. 79-80, Apr. 2004.
[39] H. Park, “Automated sleep stage analysis using hybrid rule-based and case-based reasoning,” Ph. D. Dissertation, Seoul National University, Aug. 2000.
[40] T. P. Jung, S. Makeig, M. Stensmo, and T. J. Sejnowski, “Estimating alertness from the EEG power spectrum,” IEEE Trans. Biomed Eng, Vol. 44, No. 1, pp. 60-69, 1997.
[41] S. Makeig, “Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones,” Electroencephalog. clin. Neurophysiolog., Vol. 86, pp. 283-93, 1993.
[42] S. Makeig, and M. Inlow, “Lapses in alertness: Coherence of fluctuations in performance and EEG spectrum,” Electroencephalography and Clinical Neurophysiology, Vol. 86, pp. 23-35, 1993.
[43] S. Makeig, M. Westerfield, J. Townsend, T. P. Jung, E. Courchesne, and T. J. Sejnowski,
“Independent components of early event-related potentials in a visual spatial attention task,” Philosophical Transactions: Biological Sciences, Vol. 354, pp. 1135-1144, 1999.
[44] S. Makeig, S. Enghoff, T. P. Jung, and T. J. Sejnowski, “Moving-window ICA decomposition of EEG data reveals event-related changes in oscillatory brain activity,”
[46] J. C. Woestenburg, M. N. Verbaten, and J .L. Slangen, “The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain,”
Biological Psychology, Vol. 16, pp. 127-47, 1983.
[47] P. J. Oster and J. A. Stern, “Measurement of eye movement electrooculography,”
Techniques in Psychophysiology,” Wiley Chichester, pp. 275-309, 1980.
[48] T. P. Jung, C. Humphries, T. W. Lee, S. Makeig, M. J. McKeown, V. Iragui, and T. J.
Sejnowski, “Extended ICA removes artifacts from electroencephalographic recordings,” Advances in Neural Information Processing Systems Vol. 10, pp. 894-900, 1998.
[49] S. Makeig and T. P. Jung, “Tonic, phasic and transient EEG correlates of auditory awareness in drowsiness,” Cogn. Brain Res. Vol. 4, pp. 15-25, 1996.
[50] M. Treisman, (J. Gibbon and L. Allan. Eds.) “Temporal rhythms and cerebral rhythms,”
Timing and Time Perception, New York: Academic, Vol. 423, pp. 542-565, 1984.
[51] S. Makeig and T. P. Jung, “Changes in alertness are a principal component of variance in the EEG spectrum,” NeuroReport, Vol. 7, pp. 21316, 1995.
[52] J. Beatty, A. Greenberg, W. P. Deibler, and J. O'Hanlon, “Operant control of occipital theta rhythm affects performance, in a radar monitoring task,” Science, Vol. 183, pp.
871-873, 1974.
[53] L. Evans, Traffic Safety and the Driver, van Nostrand Reinhold (ed.), New York, 1991.
[54] L. Y. Chang and F. Mannering, “Analysis of injury severity and vehicle occupancy in truck-non-truck-involved accidents,” Accident Analysis and Prevention, Vol. 31, pp.
579-592, 1999.
[55] L. P. Kostyniuk, F. M. Streff, and J. Zakarajsek , “Identifying unsafe driver actions that lead to fatal car-truck crashes,” AAA Foundation for Traffic Safety, 2002.
[56] J. Hendrix, “Fatal crash rates for tractor-trailers by time of day,” Proceedings of the International Truck and Bus Safety Research and Policy Symposium, pp. 237-250, 2002.
[57] URL: http://www.nhtsa.dot.gov/
[58] URL: http://www.sleepfoundation.org/
[59] J. Cremer, J. Kearney, and Y. Papelis, “Driving simulation: challenges for VR technology,” IEEE Computer and Applic., Vol. 16, No. 5, pp. 16-20, 1996.
[60] F. P. Brooks, “What’s real about Virtual Reality?” IEEE Computer Graphics and Applic., Vol. 19, No. 6, pp. 16-27, 1996.
[61] C. F. Hsu, C. T. Lin, T. Y. Huang, and K. Y. Young “Development of multipurpose
virtual-reality dynamic simulator with force-reflection joystick,” Journal of Systems and Control Engineering, 2004.
[62] C. T. Lin, I. F. Chung, and J. Y. Lin, “Multipurpose virtual-reality-based motion simulator,” Proceeding of 2001 IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, Oct. 2001.
[63] C. T. Lin, Y. T. Wang, T. Y. Huang, and C. F. Hsu, “Research of multi-function hla-based virtual-reality simulation system,” National Defense Conference, pp. 185-196, R.O.C., 2003.
[64] WorldToolKit Reference Manual, Release 7, SENSE8 Corporation, Mill Valley, USA, 1997.
[65] P. Comon, “Independent component analysis — A new concept?” Signal Processing, Vol. 36, pp. 287–314, 1994.
[66] J. F. Cardoso, and B. Laheld, “Equivariant adaptive source separation,” IEEE Transactions on Signal Processing, Vol. 45, pp. 434–444, 1996.
[67] C. T. Lin, R. C. Wu, S. F. Liang, and T. P. Jung, “Monitoring driver's alertness based on the driving performance estimation and the EEG power spectrum,” Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.
[68] D. Ghahremani, S. Makeig, T. P. Jung, A. J. Bell, and T. J. Sejnowski. “Independent component analysis of simulated EEG using a three-shell spherical head model,” Tech Rep. INC-9601, Institute for Neural Computation, University of California, San Diego, 1996.
[69] S. Makeig, T. P. Jung, D. Ghahremani, and T, J, Sejnowski, “Independent component analysis of simulated ERP data,” Tech Rep. INC-9606, Institute for Neural Computation, University of California, San Diego, 1996.
[70] M. Scherg, and DVon Cramon, “Evoked dipole source potentials of the human auditory cortex,” Electroencephalography and Clinical Neurophysiology, Vol. 65, No. 5, pp.
344-60, 1996.
[71] A. M. Dale, and M. I. Sereno, “Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach,” Journal
[73] S. Makeig, T. P. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski. “Blind separation of event-related brain responses into independent components,” Proc. Natl. Acad. Sci., Vol. 94, pp. 10979-84, USA, 1997.
[74] S. Makeig, M. Westerfield, J. Townsend, T. P. Jung, E. Courchesne, and T. J. Sejnowski,
“Independent components of early event-related potentials in a visual spatial attention task,” Philosophical Transactions: Biological Sciences, Vol. 354, pp. 1135-44, 1999.
[75] S. Makeig, S. Enghoff, T. P. Jung, and T. J. Sejnowski, “An efficient basis for brain-actuated control,” IEEE Trans Rehab Eng., Vol. 8, pp. 208-211, 2000.
[76] S. Makeig, T. P. Jung, and T. J. Sejnowski, “Awareness during drowsiness: dynamics and electrophysiological correlates,” Canadian J. Experimental Psychology, Vol. 54, No. 4, pp. 266-73, 2000.
[77] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T. J. Sejnowski,
“Analyzing and visualizing single-trial event-related potentials,” Advances in Neural Information Processing Systems, Vol. 11, pp. 118-24, 1999.
[78] T. P. Jung, S. Makeig, C. Humphries, T. W. Lee, M. J. McKeown, V. Iragui, T. J.
Sejnowski, “Removing electroencephalographic artifacts by blind source separation,”
Psychophysiology, Vol. 37, pp. 163-78, 2000.
[79] R. N. Vigário, “Extraction of ocular artifacts from EEG using independent component analysis,” Electroencephalography and Clinical Neurophysiology, Vol. 103, No. 3, pp.
395-404, 1997.
[80] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T. J. Sejnowski,
“Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects,” Clinical Neurophysiology, Vol. 111, No. 10, pp. 1745-58, 2000.
[81] M. J. McKeown, S. Makeig, G. G. Brown, T. P. Jung, S. S. Kindermann, A. J. Bell, and T. J. Sejnowski, “Analysis of fMRI data by blind separation into independent spatial components,” Human Brain Mapping, Vol. 6, No. 3, pp. 160-88, 1998.
[82] P. Nunez, Electric Fields of the Brain, Oxford University Press, 1981.
[83] P. Fries, J. Reynolds, A. Rorie, and R. Desimone, “Modulation of oscillatory neuronal synchronization by selective visual attention,” Science, Vol. 291, No. 5508, pp. 1560-63, 2001.
[84] E. Werth, P. Achermann, and A. A. Borbély, “Fronto-occipital EEG power gradients in human sleep,” Journal of Sleep Research, Vol. 6, No. 2, pp. 102-12, 1997.
[85] A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, Vol. 7, pp. 1129–1159,
1995.
[86] T. W. Lee, M. Girolami, and T. J. Sejnowski, “Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources,”
Neural Computation, Vol. 11, pp. 606–633, 1999.
[87] A. Papoulis, “Minimum bias windows for high resolution spectral estimation,” IEEE Trans. Inform. Theory, Vol. IT-19, pp. 9-12, 1973.
[88] M. Steriade, “Central core modulation of spontaneous oscillations and sensory transmission in thalamocortical systems,” Current Opinion in Neurobiol., Vol. 3, No. 4, pp. 619-625. 1993.
[89] A. Destexhe, D. Contreras, and M. Steriade, “Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states,” J Neurosci, Vol. 19, pp. 4595-4608, 1999.
[90] P. Achermann, and A. A. Borbely, “Low frequency oscillations in the human sleep electroencephalogram,” Neuroscience, Vol. 81, pp. 213-222, 1997.
[91] E. Werth, P. Achermann, D. J. Dijk, A. A. Borbely, “Spindle frequency activity in the sleep EEG: individual differences and topographic distribution,” Electroencephalogr Clin Neurophysiol, Vol. 103, pp. 535-542, 1997.
[92] D. Gervasoni, S. C. Lin, S. Ribeiro, E. S. Soares, J. Pantoja, and M. A. L. Nicolelis,
“Global forebrain dynamics predict rat behavioral states and their transitions,” Journal of Neuroscience, Vol. 24, pp. 11137–11147, Dec. 2004.
[93] C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996.
[94] C. F. Juang and C. T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. on Fuzzy Systems, Vol. 6, No. 1, pp. 12-32, 1998.
[95] A. Kemeny and F. Panerai1, “Evaluating perception in driving simulation experiments,”
TRENDS in Cognitive Sciences, pp. 31-37, Vol. 7, No. 1, Jan. 2003.
[96] S. Makeig, A. Delorme, M. Westerfield, J. Townsend, E. Courchense, and T. J.
Sejnowski, “Electroencephalographic brain dynamics following visual targets requiring manual responses,” PLOS Biology, Vol. 2, No. 6, pp. 747-62, 2004.
[97] Z. Duric, W. D. Gray, R. Heishman, L. Fayin, A. Rosenfeld, M. J. Schoelles, C. Schunn,
cognitive behavior at man-machine interface,” Proceedings of 4th IEEE International Workshop on Robot and Human Communication, pp. 71-76, TOKYO, Jul. 1995.
[99] K. Murano, “Estimation of a plant operator's cognitive state,” Proceedings of International Conference on Industrial Electronics, Control, and Instrumentation, pp.
371-375, Vol. 1, Nov. 1993.
[100] M. Takahashi, O. Kubo, M. Kitamura, H. Yoshikawa, “Neural network for human cognitive state estimation,” Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, Vol. 3, pp. 2176-2183, Sep. 1994.
[101] M. LeCavalier, G. E. Miller, and W. R. Klemm, “Analysis of evoked EEG patterns from controlled cognitive states,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 3, pp. 1196-1197, Nov. 1988.
[102] D. D. Schmorrow and A. A. Kruse, “DARPA's augmented cognition program-tomorrow's human computer interaction from vision to reality: building cognitively aware computational systems,” Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants, pp. 7/1-7/4, Sep. 2002.
[103] H. V. Rosvold, A. F. Mirsky, I. Sarason, E. D. Bransome Jr., and L. H. Beck, “A continuous performance test of brain damage,” J. Consult. Psychol., Vol. 20, 1956.
[104] H. V. Semlitsch, P. Anderer, P. Schuster, and O. Presslich, “A solution for reliable and valid reduction of ocular artifacts applied to the P300 ERP,” Psychophy., Vol. 23, pp.
695-703, 1986.
[105] T. Kohonen, G. Barna, and R. Chrisley, “Statistical pattern recognition with neural networks: benchmarking studies,” Proceedings of the International Conference on Neural Networks, pp. 61-68, Los Alamitos, 1988.
[106] T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995.
[107] H. Demuth and M. Beale, Neural Network Toolbox For Use with MATLAB, 3ed, The MathWorks Inc., 1998.
[108] C. T. Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning, World Scientific, 1994.
[109] M. S. Lewicki, “A review of methods for spike sorting: the detection and classification of neural action potentials,” Network: Computation Neural Syst., Vol. 9, pp. 53-78, 1998.
[110] J. H. Choi, D. Kim, and T. Kim, “A new overlapping resolution method for multi-channel spike sorting,” The 2nd IEEE EMBS Conference on Neural Engineering, Mar. 2005.
[111] A. M. Mamlouk, H. Sharp, K. Menne, U. G. Hofmann, and T. Martinetz, “Unsupervised spike sorting with ICA and its evaluation using genesis simulations,” Neurocomputing, 2005.
[112] P. Philipa, J. Taillarda, E. Kleinb, P. Sagaspeb, A. Charlesb, W. L. Daviesc, C.
Guilleminaultd, and B. Bioulaca, “Effect of fatigue on performance measured by a driving simulator in automobile drivers,” Journal of Psychosomatic Research, Vol. 55 pp. 197-200, 2003.
[113] S. Chatterjee and A. S. Hadi, “Influential observations, high leverage points, and outliers in linear regression,” Statistical Science, pp. 379-416, 1986.
[114] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.
[115] D. K. McGregor, and J. A. Stern, “Time on task and blink effects on saccade duration,”
Ergonomics, Vol. 39, pp. 649–660, 1996.
[116] N. R. Draper and H. Smith, Applied Regression Analysis, Wiley Series in Probability and Statistics, 1998.
姓名: 吳瑞成 性別: 男
生日: 中華民國 62 年 4 月 1 日 籍貫: 台灣省高雄縣
論文題目: 中文: 基於腦波之駕駛員認知反應估測及其在安全駕駛的應用 英文: EEG-Based Assessment of Driver Cognitive Responses and Its
論文題目: 中文: 基於腦波之駕駛員認知反應估測及其在安全駕駛的應用 英文: EEG-Based Assessment of Driver Cognitive Responses and Its