فهرست:
فصل اول: مقدمه......................................................................................................................................
1
1-1- مقدمه ...............................................................................................................................................
2
1-2- تعریف مسئله ....................................................................................................................................
3
1-3- نگاهی به فصول پایان نامه....................................................................................................................
4
فصل دوم: پیشینه تحقیقات.....................................................................................................................
6
2-1- روشهای موجود برای تشخیص خستگی ...........................................................................................
9
2-1-1- روشهای مبتنی بر تحلیل طیف سیگنال EEG ..............................................................
9
2-1-2- روشهای مبتنی بر تحلیل تغییرات در آنتروپی سیگنال EEG ........................................
12
2-1-3- روشهای مبتنی بر تحلیل نظم منطقی بین نواحی مختلف مغز.......................................
14
2-1-4- روشهای مبتنی بر دادن تحریک به فرد در حین فعالیت.................................................
15
2-2- تاریخجه و نحوه ثبت سیگنال EEG....................................................................................................................
16
2-3- جمع بندی ..................................................................................................................................................................
20
فصل سوم: روش تحقیق...........................................................................................................................
21
3-1- مقدمه ..................................................................................................................................................
22
3-2- نویزهای سوار شده بر روی سیگنال EEG و نحوه کاهش اثر آنها .....................................................
23
3-2-1- امواج ناخواسته زیستی ....................................................................................................
23
3-2-2- امواج ناخواسته محیطی ...................................................................................................
24
3-2-1- پیش پردازش ..................................................................................................................
24
3-3- مدل سیگنال .......................................................................................................................................
24
3-4- انتخاب الکترود مرجع ..........................................................................................................................
26
3-5- مشخص کردن تعداد منابع تولید کننده سیگنال .................................................................................
27
3-6- مکان یابی در فضای پرتوسازی ............................................................................................................
30
3-6-1- فیلتر کردن فضایی با محدودیت کمترین واریانس............................................................
31
3-6-2- مشکل روش LCMV........................................................................................................
35
عنوان صفحه
3-6-3- روش پیشنهادی برای مکان یابی .....................................................................................
36
3-7- محاسبه همبستگی در سیگنال EEG ..................................................................................................
38
3-8- ویژگی استفاده شده برای تشخیص خستگی .......................................................................................
40
3-9- روشهای کلاسه بندی استفاده شده ..................................................................................................
40
3-9-1- ماشین بردار پشتیبان ......................................................................................................
40
3-9-2- k نزدیک ترین همسایه ....................................................................................................
42
3-10- روشهای مقایسه شده با روش پیشنهادی..........................................................................................
42
3-10-1- آنتروپی تقریبی .............................................................................................................
43
3-10-2- کولموگروف آنتروپی.......................................................................................................
44
3-10-3- تجزیه و تحلیل بردار اصلی به همراه کرنل ....................................................................
45
3-10-4- مدل مخفی مارکوف.......................................................................................................
45
3-10-5- روش اراﺋﻪ شده توسط لیو و همکارانش .........................................................................
46
3-10-6- روش اراﺋﻪ شده توسط شن و همکارانش .......................................................................
46
3-10-7- توموگرافی الکترومغناطیسی با رزولوشن پایین ..............................................................
47
3-10-8- توموگرافی الکترومغناطیسی استاندارد با رزولوشن پایین ..............................................
48
3-11- جمعبندی .........................................................................................................................................
49
فصل چهارم: آزمایشها و نتایج ..................................................................................................................
50
4-1- مقدمه ..................................................................................................................................................
51
4-2- شبیه سازی سیگنال EEG برای مشخص کردن دقت مکانیابی ........................................................
52
4-3- سیگنال EEG ثبت شده برای بررسی میزان خستگی .........................................................................
53
4-4- شبیه سازی سیگنال EEG برای بررسی میزان خستگی ......................................................................
57
4-5- نتایج ....................................................................................................................................................
59
4-5-1- مقایسه روش مکان یابی پیشنهادی و LCMV ................................................................
59
4-5-2- بررسی خستگی به کمک داده های ثبت شده EEG.......................................................
60
4-5-2-1- بررسی مکان و قدرت منابع در حالت خسته و نرمال....................................
60
4-5-2-2- بررسی ویژگی پیشنهادی در کلاسهبندی حالتها........................................
62
4-5-2- بررسی خستگی به کمک سیگنال شبیهسازی شده..........................................................
67
4-6- جمعبندی ............................................................................................................................................
70
فصل هفتم: نتیجه گیری و پیشنهادات ................................................................................................
71
فهرست منابع ..........................................................................................................................................
74
منبع:
Grandjean, E. (1981). “Fitting the Task to the Man.” London: Taylor & Francis, 4th Edition
Idogawa, K. (1991). “One the brain wave activity of professional drivers during monotonous work.” Behaviourmetrika, vol. 30: 23-34.
Linder, D., Frese, M., Meijman, T. F. (2003). “Mental fatigue and the control of cognitive process: effects on perseveration and planning.” ActaPsychologica, vol. 113: 45–65.
Arnedt, J.T., Geddes, M.A.C., Maclean, A.W. (2005). “Comparative sensitivity of a simulated driving task to self-report, physiological, and other performance measures during prolonged wakefulness.” J. Psychosom. Res., vol. 58: 61–71.
Lal, S.K.L., Craig, A., Boord, P., Kirkup, L., Nguyen, H. (2003). “Development of an algorithm for an EEG based driver fatigue countermeasure.” Journal of Safety Research, vol. 34: 321–328.
Caton R. (1875). “The electric currents of the brain.” Br. Med. J, vol. 2.
Berger, H. (1929). “On the Electroencephalogram of Man.” Journal fur Psychology and Neurology, vol. 40: 160–179.
Rechtschaffen, A., Kales, A. (1968). “A Manual of Standardized Terminology, Techniques and Scorings System for Sleep Stages of Human Subjects”, Public Health Service: Bethesda.
Lal, S.k.L., Craig, A. (2002). “Driver fatigue: Electroencephalography and psychological assessment.” Psychophysiology, vol. 39: 313-321.
Sekihara K., Sahani, M., Nagarajan, S.S., (2005). “Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction.” Neuro Image, vol. 25: 1056-1067.
Greenfield, S. (2000), “The private life of the brain, New York” John Wiley & Sons.
Linder, D., Frese, M., Meijman, T.F. (2003). “Mental fatigue and the control of cognitive process: effects on perseveration and planning.” ActaPsychologica, vol. 113: 45–65.
Mallis, M.M. (1999). “Evaluation of techniques for drowsiness detection: Experiment on performance-based validation of fatigue-tracking technologies.” Drexel University, vol. 21: 210–215.
Shen, K.Q., Ong, C.J., Li, X.P., Zheng, H., Wilder-Smith, E.P.V. (2007). “A feature selection method for multilevel mental fatigue EEG classification.” IEEE: Transactions of biomedical engineering, vol. 54: 1231-1237.
Bittner, R., Hana, K., Pousek, L., Smrha, P., Schreib, P., Vysuky, P. (2000). “Detecting of fatigue state of a car driver.” Lect. notes Comp. Sci, vol. 1933: 123–126.
Liu, J., Zhang, C., Zheng, C. (2010). “EEG-based estimation of mental fatigue by using KPCA–HMM and complexity parameters.” Biomedical Signal Processing and Control, vol.5: 124–130.
Pincus, S.M. (1991). “Approximate entropy as a measure of system complexity.” Proceedings of the National Academy of Science United States of America, vol. 88: 2297–2301.
Rezek, I.A., Roberts, S.J. (1998). “Stochastic complexity measures for physiological signal analysis.” IEEE Transactions on Biomedical Engineering vol.45: 1186–1191.
Aba´ solo, D., Hornero, R., Espino, P., Poza,J., Sa´nchez, C.I., de la Rosa, R. (2005). “Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with Approximate Entropy.” Clinical Neurophysiology, vol.116: 1826–1834.
Hong, B., Yang, F.S., Tang, Q.Y., Chan, T.C.” Approximate entropy and its preliminary application in the field of EEG and cognition.” in: Proceedings of the 20th Annual International Conference of the IEEE EMBS, vol.20: 2091–2094.
Lempel, A., Ziv, J. (1976). “On the complexity of finite sequence.” IEEE Transactions on Information Theory, vol.22: 75–81.
ten Caat, M. (2008). “Multichannel EEG Visualization Master’s thesis.” Wiskunde Institute of Mathematics and Computing Science.
Halliday, D.M., Rosenberg, J. R., Amjad, A. M.,Breeze, P., Conway, B. A.,Farmer, S.F. (1995). “A framework for the analysis of mixed time series/point process data theory and application to the study of physiological tremor, single motor unit discharges and electromyograms.” Prog Biophys Mol Bio., vol. 64: 237–278.
Stein, A.v., Rappelsberger, P., Sarnthein, J., Petsche, H. (1999). “Synchronization between temporal and parietal cortex during multimodal object processing in man.” Cereb Cortex , vol.9: 137–150.
Kaminski, M., Blinowska, K., Szelenberger, W. (1997). “Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness.” Electroen Clin Neuro, vol. 102: 216–227.
Jarchi, D., Sanei, S., Principe, J. C. , Makkiabadi, B. (2010). “A New Spatiotemporal Filtering Method for Single-trial Estimation of Correlated ERP Subcomponents.” IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2010.2083660.
Spyrou, L., Jing, M., Sanei, S., Sumich, A. (2007). “Separation and localisation of P300 sources and their subcomponents using constrained blind source separation.” EURASIP Journal on Advances in Signal Processing, vol. 2007: 10 pages.
Nunez, P., katznelson, R. (1981). “Electric field of the brain: the neurophysis of EEG.” Oxford univ: New York.
Sameni, R., Jutten, C., Shamsollahi, M.B. (2010) “A deflation procedure for subspace decomposition.” IEEE Transactions on Signal Processing, vol. 58: 2363–2374.
Desmedt, J.E., Tomberg, C., Noel, P., Ozaki, I. (1990). “Beware of the average reference in brain mapping (Review).” Electroencephalogr Clin Neurophysiol Suppl, vol. 4: 22–7.
Gencer, N.G., Williamson, S.J., Gueziec, A., Hummel, R. (1996). “Optimal reference electrode selection for electric source imaging.” Electroencephalogr Clin Neurophysiol, vol. 99: 163–73.
Pataraia, E., Baumgartner, C., Lindinger, G., Deecke, L. (2002). “Magnetoencephalography in presurgical epilepsy evaluation (Review).” Neurosurg Rev, vol. 25: 141–59.
Geselowitz, D.B. (1998). “The zero of potential.” IEEE Eng Med Biol Mag, vol. 17: 128–32.
Lehmann, D., Skrandies, W. (1980). “Reference-free identification of components of checkerboard-evoked multichannel potential fields.” Electroencephalogr Clin Neurophysiol, vol. 48: 609–21.
Fender, D.H. (1987). “Source localization of brain electrical activity.” handbook of electroencephalography and clinical neurophysiology, vol. 1: 355–99.
Murray, M.M., Michel, C.M., Grave de Peralta, R., Ortigue, S., Brunet, D., Andino, S.G., Schnider, A., (2004). “Rapid discrimination of visual and multisensory memories revealed by electrical neuroimaging.” Neuroimage , vol.21: 125–35.
Wang, K., Zhang, Q., Reilly, J., Yip, P.(1990). “On information theoretic criteria for Determining the number of signals in high resolution array processing.” IEEE Trans. Signal Process., vol.38: 1959–1971.
Uijen, G., Van Oosterm A., (1992). “On the detection of the number of signals in multilead ECGs.” Meth. Inform. Med., vol.31:247–255.
Knösche, T., Berends, E., Jagers, H., Peters, M. (1998). “Determining the number of independent sources of the EEG.” A simulation study on information criteria, Brain Topogr., vol.11: 111–124.
Mosher, J.C., Lewis, P.S., Leahy, R.M. (1992) “Multiple dipole modeling and localization from spatio-temporal MEG data.” IEEE Trans Biomed Eng, vol.39: 541–57.
Xu, X.L., Xu, B., He, B. (2004). “An alternative subspace approach to EEG dipole source localization.” Phys. Med. Biol., vol.49:327–343.
Kwek, K.T. (2001). “Accuracy of model selection criteria for a class of autoregressive conditional heteroscedastic models.” FEA Working Paper, vol.1.
Van Veen, B.D., van Drongelen, W., Yuchtman, M., Suzuki, A. (1997). “Localization of brain electrical activity via linearly constrained minimum variance spatial filtering.” IEEE Trans. Biomed. Eng., vol.44: 867–880.
Sekihara, K.S., Nagarajan, S.S., Poeppel, D., Marantz, A., Miyashita, Y. (2001). “Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique.” IEEE Trans. Biomed. Eng., vol. 48: 760–771.
Robinson, S.E., Vrba, J. (1999). “Functional neuroimaging by synthetic aperture magnetometry (SAM).”, in Recent Advances in Biomagnetism, Sendai, Japan: Tokio Univ, 302–305.
Sekihara, K.S., Nagarajan, S.S., Poeppel, D., Marantz, A. (2004). “Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction.” IEEE Trans. Biomed. Eng., vol. 51: 1726–1734.
Baryshnikov, B.V., Van Veen, B.D., Wakai, R.T. (2004) “Maximum likelihood estimation of lowrank signals for multiepoch MEG/EEG analysis.” IEEE Trans. Biomed. Eng., Vol. 51: 1981–1993.
Li, X., Cui, D., Jiruska, P., Fox, J.E., Yao, X., Jefferys, J.G. (2007). “Synchronization easurement of multiple neuronal populations.” J. Neurophysiol, vol. 98: 3328–3341.
Schreiber, T., Schmitz, A. (1996). “Improved surrogate data for nonlinearity tests.” Phys.Rev. Lett., vol. 77: 635–638.
Vapnik, V. (1998). “Statistical Learning Theory.” Wiley: New York.
Fix, E., Hodges, J.L. (1999). “Discriminatory analysis, non-parametric discrimination, consistency properties.” USAF Sch. Aviation Medicine, Randolph Field: Tex.
Loftsgaarden, D.O., Quesenberry, C.P. (1965) “A nonparametric density function.” Ann. Math. Statist., vol. 36: 1049-1051.
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B. (2001) “An introduction to kernel based learning algorithms.” IEEE Transactions on Neural Networks, vol. 12: 181–201.
Harmeling, S., Ziehe, A., Kawanble, M. (2003). “Kernel based nonlinear blind separation.” Neural computation, vol. 15: 1089–1124.
Cao, B., shen, D., Shun, J.T., Yang, Q., Chen, Z. (2007). “Feature selection in a kernel space.” ICML, 121–128.
Lloyd, Stuart, P. (1982). “Least squares quantization in PCM.” IEEE Transactions on Information Theory, vol. 28: 129–137.
Dempster, A.P., Laird, N.M., Rubin, D.B., (1997) “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the Royal Statistical Society, vol. 39: 1–38.
Viterbi, A.J. (1967). “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm.” IEEE Transactions on Information Theory, vol. 13: 260–269.
Breiman, L. (1996). “Bagging predictors.” Mach. Learn., vol. 26: 123–140.
Breiman, L. (2001). “Random forests.” Mach. Learn., vol. 45: 5–32.
Pasqual-Marqui, R.D., Michel, C.M., Lehmann, D. (1994). “Low resolution electromagnetic tomography: a new method to localize electrical activity in the brain.” Int J Psychophysiol. Vol. 18: 49–65.
Pascual-Marqui, R.D. (2002). “Standardized low resolution brain electromagnetic tomography (sLORETA).” technical details, Methods Findings Exp Clin Pharmacol, vol. 24: 5–12.
Butcher, J.N., Dahlstrom, W.G., Graham, J.R., Tellegen, A., Kaemmer, B. (1989). “MMPI-2: Manual for Administration, Scoring and Interpretation.” Minneapolis: University of Minnesota Press.
Pfurtscheller, G., Stancak, A., Neuper, Ch. (1996) “Event-related synchronization (ERS) in the alpha band-an electrophysiological correlate of cortical idling.” International Journal of Psychophysiology, vol. 24: 39–46.