腦電波與腦磁波訊號已被廣泛地應用於大腦神經科學學術研究與臨床診斷。然而，非侵入式的腦電/磁波資料，可能夾帶不同頻帶的自發性腦波律動，生理雜訊和系統雜訊。如何從混合訊號中萃取出感興趣之腦波特徵是相當重要的課題。本論文主旨為開發以經驗模態分解法為基礎之腦電/磁波分析技術，達到單一試驗分析腦電/磁波的目的。經驗模態分解法根據資料本身的特質，自適性的拆解出不同階層之訊號。根據各個應用設計出事件相關成分辨識流程，以降低非事件相關成分之干擾。本研究中，利用經驗模態分解法分別應用於視覺腦波大腦人機界面、大腦感覺運動皮質區mu波萃取與嗅覺誘發電位萃取。
在頻率特徵萃取的應用中，本研究利用穩態視覺誘發腦電波(steady-state visual evoked potential, SSVEP)訊號做為輸入訊號，利用經驗模態分解法與refined generalized zero-crossing (rGZC)萃取與辨識短時間腦波訊號，經由EMD與rGZC方法辨識穩態視覺誘發電位，有效率的提升視覺腦波大腦人機介面的正確性與辨識速度，增加訊息傳輸率(information transfer rate, ITR)。
在感覺運動區mu波萃取應用中，本研究開發以經驗模態分解法為基礎之單一試驗(single-trial)的腦波律動萃取方法。由於事件相關非同步(Event-related desynchronization, ERD)與同步(synchronization, ERS)腦波律動分析方法已經被應用於大腦感覺運動的功能性研究上，已證明感覺運動皮質區Mu波的ERD主要與運動的籌畫和啟動有關，而ERS則主要與運動的停止或抑制有關。利用經驗模態分解法萃取mu波律動，觀察受試者每一次試驗腦波律動的反應，將有助於腦波律動機轉與帕金森氏的臨床診斷的研究。
由於嗅覺事件相關電位（OERP）在臨床嗅覺功能診斷具有直接反應的神經活動和高時間解析度率等優點。然而，研究嗅覺誘發電位必須重複次激提升訊雜比與去除自發性慢波。本研究開發了一種基於經驗模態分解方法將時域信號分解成有限數量總和的固有模態函數（IMF分量）。利用頻率與空間分布的特徵選擇嗅覺相關之IMF分量並重建雜訊抑制之嗅覺誘發電位。

Electroencephalography (EEG)/Magnetoencephlography(MEG) has been applied for investigation of neuroscience and diagnosis of many neurological disorders. The noninvasive EEG recordings are overlapping potentials from spontaneous brain rhythm, physiological artifact and external interference. Accordingly, extraction task-related signal features is crucial in the field of EEG/MEG signal processing. The aim of this study is to develop an EMD-based approach to extract single-trial EEG signal. The EMD method decomposed an EEG/MEG epoch into various scale of sub-band component called intrinsic mode function (IMF) and customized the recognition of task-related component to suppress the task-unrelated component. This dissertation evaluates the performance of EMD in EEG or MEG signal into the applications of steady-state visual evoked potential based brain computer interface (SSVEP-based BCI), sensorimotor mu rhythm extraction and olfactory event-related potential extraction.
To evaluate the performance of SSVEP feature extraction, this study presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in SSVEP-based BCI. The EMD-rGZC improves the information transfer rate (ITR).
Event-related desynchronization (ERD) and synchronization (ERS) analysis methods have been widely used in studying movement-related sensorimotor functions in human brain. Movement-related ERD is functionally related to motor planning and initialization, while movement-related ERS is related to motor inhibition and motor cortex resetting. This study developed a single-trial brain rhythm analysis method based on EMD method to discover the mechanisms of ERD and ERS in normal subjects and Parkinson’s patients as well, which could be used as a clinical index for diagnosing Parkinson’s patients.
In the study of olfactory event-related potential (OERP) feature extraction, we developed an EMD-based approach to extract OERP from multi-channel EEG recordings. The EMD approach decomposes a signal into IMFs by iteratively conducting the sifting process. Dual criteria on frequency and spatial template were adopted to facilitate the selection of OERP-related IMFs and to reconstruct single-trial OERP for inter-trial investigation.

[1] S. Sanei, and J. A. Chambers, EEG Signal Processing: Wiley, 2008.
[2] A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals and systems: Prentice Hall, 1997.
[3] G. Pfurtscheller, C. Brunner, A. Schlogl, and F. H. Lopes da Silva, “Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks,” Neuroimage, vol. 31, no. 1, pp. 153-159, 2006.
[4] P. L. Lee, Y. T. Wu, L. F. Chen, Y. S. Chen, C. M. Cheng, T. C. Yeh et al., “ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization,” Neuroimage, vol. 20, no. 4, pp. 2010-2030, 2003.
[5] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc R Soc Lond A, vol. 454, no. 1971, pp. 903-995, 1998.
[6] P. Flandrin, G. Rilling, and P. Goncalves, “Empirical mode decomposition as a filter bank,” IEEE Signal Process Lett, vol. 11, no. 2, pp. 112-114, 2004.
[7] Z. Wu, N. E. Huang, S. R. Long, and C.-K. Peng, “On the trend, detrending, and variability of nonlinear and nonstationary time series,” Proc Nat Acad Sci USA, vol. 104, no. 38, pp. 14889-14894, 2007.
[8] W. Huang, Z. Shen, N. E. Huang, and Y. C. Fung, “Use of intrinsic modes in biology: Examples of indicial response of pulmonary blood pressure to ± step hypoxia,” Proc Nat Acad Sci USA, vol. 95, no. 22, pp. 12766-12771, 1998.
[9] R. Balocchi, D. Menicucci, E. Santarcangelo, L. Sebastiani, A. Gemignani, B. Ghelarducci et al., “Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition,” Chaos, Solitons Fractals, vol. 20, no. 1, pp. 171-177, 2004.
[10] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc R Soc A, vol. 454, no. 1971, pp. 903-995, 1998.
[11] W. Huang, Z. Shen, N. E. Huang, and Y. C. Fung, “Engineering analysis of biological variables: An example of blood pressure over 1 day,” Proc Nat Acad Sci USA, vol. 95, pp. 4816-4821, 1998.
[12] N. Stevenson, M. Mesbah, and B. Boashash, "A sampling limit for the empirical mode decomposition." pp. 647-650.
[13] N. E. B. Huang, MD, US), Computing frequency by using generalized zero-crossing applied to intrinsic mode functions, United States,to The United States of America as represented by the Administrator of the National Aeronautics and Space Administration (Washington, DC, US), 2006.
[14] G. Rilling, and P. Flandrin, "Sampling effects on the Empirical Mode Decomposition," HAL - CCSD, 2006.
[15] C. S. Herrmann, “Human EEG responses to 1-100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena,” Exp Brain Res, vol. 137, no. 3-4, pp. 346-353, 2001.
[16] M. A. Pastor, J. Artieda, J. Arbizu, M. Valencia, and J. C. Masdeu, “Human cerebral activation during steady-state visual-evoked responses,” J Neurosci, vol. 23, no. 37, pp. 11621-11627, 2003.
[17] Y. Wang, R. Wang, X. Gao, B. Hong, and S. Gao, “A Practical VEP-based Brain-Computer Interface,” IEEE Trans Neural Syst Rehabil Eng, vol. 14, no. 2, pp. 234-239, 2006.
[18] M. Cheng, X. Gao, S. Gao, and D. Xu, “Design and implementation of a brain-computer interface with high transfer rates,” IEEE Trans Biomed Eng, vol. 49, no. 10, pp. 1181-1186, 2002.
[19] S. P. Kelly, E. C. Lalor, R. B. Reilly, and J. J. Foxe, “Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication,” IEEE Trans Neural Syst Rehabil Eng, vol. 13, no. 2, pp. 172-178, 2005.
[20] E. C. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith, R. B. Reilly et al., “Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment,” EURASIP JASP, vol. 19, pp. 3156-3164, 2005.
[21] G. R. Muller-Putz, E. Eder, S. C. Wriessnegger, and G. Pfurtscheller, “Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI,” J Neurosci Methods, vol. 168, no. 1, pp. 174-181, 2008.
[22] G. R. Muller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, “Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components,” J Neural Eng, vol. 2, no. 4, pp. 123-130, 2005.
[23] T. M. Srihari Mukesh, V. Jaganathan, and M. R. Reddy, “A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces,” Physiol Meas, vol. 27, no. 1, pp. 61-71, 2006.
[24] M. Middendorf, G. McMillan, G. Calhoun, and K. S. Jones, “Brain-computer interfaces based on the steady-state visual-evoked response,” IEEE Trans Rehabil Eng, vol. 8, no. 2, pp. 211-214, 2000.
[25] B. Z. Allison, D. J. McFarland, G. Schalk, S. D. Zheng, M. M. Jackson, and J. R. Wolpaw, “Towards an independent brain-computer interface using steady state visual evoked potentials,” Clin Neurophysiol, vol. 119, no. 2, pp. 399-408, 2008.
[26] D. Zhang, X. Gao, S. Gao, A. K. Engel, and A. Maye, “An independent brain-computer interface based on covert shifts of non-spatial visual attention,” Conf Proc IEEE Eng Med Biol Soc, vol. 2009, pp. 539-542, 2009.
[27] M. S. Treder, and B. Blankertz, “(C)overt attention and visual speller design in an ERP-based brain-computer interface,” Behav Brain Funct, vol. 6, pp. 28, 2010.
[28] L. Zhonglin, Z. Changshui, W. Wei, and G. Xiaorong, “Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs,” IEEE Trans Biomed Eng, vol. 54, no. 6, pp. 1172-1176, 2007.
[29] J. S. Barlow, “EMG artifact minimization during clinical EEG recordings by special analog filtering,” Electroen Clin Neuro, vol. 58, no. 2, pp. 161-174, 1984.
[30] D. Hagemann, and E. Naumann, “The effects of ocular artifacts on (lateralized) broadband power in the EEG,” Clin Neurophysiol, vol. 112, no. 2, pp. 215-231, 2001.
[31] P. He, G. Wilson, and C. Russell, “Removal of ocular artifacts from electro-encephalogram by adaptive filtering,” Med Biol Eng Comput, vol. 42, no. 3, pp. 407-412, 2004.
[32] T. N. Cornsweet, Visual perception: Academic Press 1970.
[33] G. Tamas, I. Szirmai, L. Palvolgyi, A. Takats, and A. Kamondi, “Impairment of post-movement beta synchronisation in parkinson's disease is related to laterality of tremor,” Clin Neurophysiol, vol. 114, no. 4, pp. 614-623, 2003.
[34] J. Decety, and F. Michel, “Comparative analysis of actual and mental movement times in two graphic tasks,” Brain Cogn, vol. 11, no. 1, pp. 87-97, 1989.
[35] C. Andrew, and G. Pfurtscheller, “Event-related coherence during finger movement: a pilot study,” Biomed Tech (Berl), vol. 40, no. 11, pp. 326-332, 1995.
[36] P. Clochon, J. Fontbonne, N. Lebrun, and P. Etevenon, “A new method for quantifying EEG event-related desynchronization:amplitude envelope analysis,” Electroencephalogr Clin Neurophysiol, vol. 98, no. 2, pp. 126-129, 1996.
[37] W. Klimesch, H. Russegger, M. Doppelmayr, and T. Pachinger, “A method for the calculation of induced band power: implications for the significance of brain oscillations,” Electroen Clin Neuro, vol. 108, no. 2, pp. 123-130, 1998.
[38] G. Pfurtscheller, and A. Aranibar, “Event-related cortical desynchronization detected by power measurements of scalp EEG,” Electroencephalogr Clin Neurophysiol, vol. 42, no. 6, pp. 817-826, 1977.
[39] G. Pfurtscheller, and F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin Neurophysiol, vol. 110, no. 11, pp. 1842-1857, 1999.
[40] G. Florian, and G. Pfurtscheller, “Dynamic spectral analysis of event-related EEG data,” Electroencephalogr Clin Neurophysiol, vol. 95, no. 5, pp. 393-396, 1995.
[41] R. Salmelin, and R. Hari, “Characterization of spontaneous MEG rhythms in healthy adults,” Electroen Clin Neuro, vol. 91, no. 4, pp. 237-248, 1994.
[42] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Analysis and visualization of single-trial event-related potentials,” Hum Brain Mapp, vol. 14, no. 3, pp. 166-185, 2001.
[43] A. Y. Kaplan, A. A. Fingelkurts, A. A. Fingelkurts, S. V. Borisov, and B. S. Darkhovsky, “Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges,” Signal Processing, vol. 85, no. 11, pp. 2190-2212, 2005.
[44] M. Hämäläinen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain,” Rev Mod Phys, vol. 65, no. 2, pp. 413-497, 1993.
[45] A. Vallabhaneni, and B. He, “Motor imagery task classification for brain computer interface applications using spatiotemporal principle component analysis,” Neurol Res, vol. 26, no. 3, pp. 282-287, 2004.
[46] M. P. Deiber, M. H. Giard, and F. Mauguiere, “Separate generators with distinct orientations for N20 and P22 somatosensory evoked potentials to finger stimulation?,” Electroencephalogr Clin Neurophysiol, vol. 65, no. 5, pp. 321-334, 1986.
[47] J. E. Desmedt, and G. Cheron, “Non-cephalic reference recording of early somatosensory potentials to finger stimulation in adult or aging normal man: differentiation of widespread N18 and contralateral N20 from the prerolandic P22 and N30 components,” Electroencephalogr Clin Neurophysiol, vol. 52, no. 6, pp. 553-570, 1981.
[48] D. S. Dinner, H. Luders, R. P. Lesser, and H. H. Morris, “Cortical generators of somatosensory evoked potentials to median nerve stimulation,” Neurology, vol. 37, no. 7, pp. 1141-1145, 1987.
[49] J. Huttunen, “Does the P35m SEF deflection really come from the motor cortex?,” Electroencephalogr Clin Neurophysiol, vol. 104, no. 1, pp. 101-102, 1997.
[50] T. D. Waberski, H. Buchner, M. Perkuhn, R. Gobbele, M. Wagner, W. Kucker et al., “N30 and the effect of explorative finger movements: a model of the contribution of the motor cortex to early somatosensory potentials,” Clin Neurophysiol, vol. 110, no. 9, pp. 1589-1600, 1999.
[51] M. Kajola, A. Ahonen, M. S. Hamalainen, J. Knuutila, O. V. Lounasmaa, J. Simola et al., “Development of multichannel neuromagnetic instrumentation in Finland,” Clin Phys Physiol Meas, vol. 12 Suppl B, pp. 39-44, 1991.
[52] J. Rosell, R. Casanas, and H. Scharfetter, “Sensitivity maps and system requirements for magnetic induction tomography using a planar gradiometer,” Physiol Meas, vol. 22, no. 1, pp. 121-130, 2001.
[53] J. A. Hartigan, and M. A. Wong, “Algorithm AS 136: A K-Means Clustering Algorithm,” J R Stat Soc Ser C Appl Stat, vol. 28, no. 1, pp. 100-108, 1979.
[54] R. Salmelin, and R. Hari, “Spatiotemporal characteristics of sensorimotor neuromagnetic rhythms related to thumb movement,” Neuroscience, vol. 60, no. 2, pp. 537-550, 1994.
[55] L. Leocani, C. Toro, P. Manganotti, P. Zhuang, and M. Hallett, “Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements,” Electroencephalogr Clin Neurophysiol, vol. 104, no. 3, pp. 199-206, 1997.
[56] G. Pfurtscheller, K. Pichler-Zalaudek, B. Ortmayr, J. Diez, and Reisecker, “Postmovement Beta Synchronization in Patients With Parkinson's Disease,” J Clin Neurophysiol, vol. 15, no. 3, pp. 243-250, 1998.
[57] J. C. Echeverria, J. A. Crowe, M. S. Woolfson, and B. R. Hayes-Gill, “Application of empirical mode decomposition to heart rate variability analysis,” Med Biol Eng Comput, vol. 39, no. 4, pp. 471-9, 2001.
[58] W. Huang, Z. Shen, N. E. Huang, and Y. C. Fung, “Nonlinear indicial response of complex nonstationary oscillations as pulmonary hypertension responding to step hypoxia,” Proc Natl Acad Sci U S A, vol. 96, no. 5, pp. 1834-1839, 1999.
[59] N. E. Huang, M.-L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen et al., “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” Proc R Soc Lond A, vol. 459, no. 2037, pp. 2317-2345, 2003.
[60] F. Nijboer, A. Furdea, I. Gunst, J. Mellinger, D. J. McFarland, N. Birbaumer et al., “An auditory brain-computer interface (BCI),” J Neurosci Methods, vol. 167, no. 1, pp. 43-50, 2008.
[61] F. Lopes da Silva, “Neural mechanisms underlying brain waves: from neural membranes to networks,” Electroencephalogr Clin Neurophysiol, vol. 79, no. 2, pp. 81-93, 1991.
[62] M. Palus, “Nonlinearity in normal human EEG: cycles, temporal asymmetry, nonstationarity and randomness, not chaos,” Biol Cybern, vol. 75, no. 5, pp. 389-396, 1996.
[63] C. Babiloni, A. Brancucci, F. Babiloni, P. Capotosto, F. Carducci, F. Cincotti et al., “Anticipatory cortical responses during the expectancy of a predictable painful stimulation. A high-resolution electroencephalography study,” Eur J Neurosci, vol. 18, no. 6, pp. 1692-1700, 2003.
[64] W. Gaetz, and D. Cheyne, “Localization of sensorimotor cortical rhythms induced by tactile stimulation using spatially filtered MEG,” Neuroimage, vol. 30, no. 3, pp. 899-908, 2006.
[65] A. Stancak, Jr., B. Feige, C. H. Lucking, and R. Kristeva-Feige, “Oscillatory cortical activity and movement-related potentials in proximal and distal movements,” Clin Neurophysiol, vol. 111, no. 4, pp. 636-650, 2000.
[66] M. L. Stavrinou, L. Moraru, L. Cimponeriu, S. Della Penna, and A. Bezerianos, “Evaluation of cortical connectivity during real and imagined rhythmic finger tapping,” Brain Topogr, vol. 19, no. 3, pp. 137-145, 2007.
[67] S. Makeig, T. P. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski, “Blind separation of auditory event-related brain responses into independent components,” Proc Natl Acad Sci U S A, vol. 94, no. 20, pp. 10979-10984, 1997.
[68] J. Gross, J. Kujala, M. Hamalainen, L. Timmermann, A. Schnitzler, and R. Salmelin, “Dynamic imaging of coherent sources: Studying neural interactions in the human brain,” Proc Natl Acad Sci U S A, vol. 98, no. 2, pp. 694-699, 2001.
[69] C. D. Tesche, M. A. Uusitalo, R. J. Ilmoniemi, M. Huotilainen, M. Kajola, and O. Salonen, “Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources,” Electroencephalogr Clin Neurophysiol, vol. 95, no. 3, pp. 189-200, 1995.
[70] G. Kobal, and C. Hummel, “Cerebral chemosensory evoked potentials elicited by chemical stimulation of the human olfactory and respiratory nasal mucosa,” Electroencephalogr Clin Neurophysiol, vol. 71, no. 4, pp. 241-250, 1988.
[71] T. S. Lorig, “The application of electroencephalographic techniques to the study of human olfaction: a review and tutorial,” Int J Psychophysiol, vol. 36, no. 2, pp. 91-104, 2000.
[72] L. Cui, and W. J. Evans, “Olfactory event-related potentials to amyl acetate in congenital anosmia,” Electroencephalogr Clin Neurophysiol, vol. 102, no. 4, pp. 303-306, 1997.
[73] C. Murphy, C. D. Morgan, M. W. Geisler, S. Wetter, J. W. Covington, M. D. Madowitz et al., “Olfactory event-related potentials and aging: normative data,” Int J Psychophysiol, vol. 36, no. 2, pp. 133-145, 2000.
[74] S. Barz, T. Hummel, E. Pauli, M. Majer, C. J. Lang, and G. Kobal, “Chemosensory event-related potentials in response to trigeminal and olfactory stimulation in idiopathic Parkinson's disease,” Neurology, vol. 49, no. 5, pp. 1424-1431, 1997.
[75] R. L. Doty, C. Li, L. J. Mannon, and D. M. Yousem, “Olfactory dysfunction in multiple sclerosis: relation to longitudinal changes in plaque numbers in central olfactory structures,” Neurology, vol. 53, no. 4, pp. 880-882, 1999.
[76] C. D. Morgan, and C. Murphy, “Olfactory event-related potentials in Alzheimer's disease,” J Int Neuropsychol Soc, vol. 8, no. 6, pp. 753-763, 2002.
[77] C. Daniels, B. Gottwald, B. M. Pause, B. Sojka, H. M. Mehdorn, and R. Ferstl, “Olfactory event-related potentials in patients with brain tumors,” Clin Neurophysiol, vol. 112, no. 8, pp. 1523-1530, 2001.
[78] M. W. Geisler, C. R. Schlotfeldt, C. B. Middleton, M. F. Dulay, and C. Murphy, “Traumatic brain injury assessed with olfactory event-related brain potentials,” J Clin Neurophysiol, vol. 16, no. 1, pp. 77-86, 1999.
[79] T. Hummel, E. Pauli, P. Schuler, B. Kettenmann, H. Stefan, and G. Kobal, “Chemosensory event-related potentials in patients with temporal lobe epilepsy,” Epilepsia, vol. 36, no. 1, pp. 79-85, 1995.
[80] P. L. Lee, L. Z. Shang, Y. T. Wu, C. H. Shu, J. C. Hsieh, Y. Y. Lin et al., “Single-trial analysis of cortical oscillatory activities during voluntary movements using empirical mode decomposition (EMD)-based spatiotemporal approach,” Ann Biomed Eng, vol. 37, no. 8, pp. 1683-1700, 2009.
[81] P. Rombaux, A. Mouraux, B. Bertrand, J. M. Guerit, and T. Hummel, “Assessment of olfactory and trigeminal function using chemosensory event-related potentials,” Neurophysiol Clin, vol. 36, no. 2, pp. 53-62, 2006.
[82] M. Rothe, Handbook of olfaction and gustation: WILEY-VCH Verlag GmbH, 2003.
[83] T. Hummel, and G. Kobal, “Chemosensory event-related potentials to trigeminal stimuli change in relation to the interval between repetitive stimulation of the nasal mucosa,” Eur Arch Otorhinolaryngol, vol. 256, no. 1, pp. 16-21, 1999.
[84] L. Wang, V. E. Walker, H. Sardi, C. Fraser, and T. J. C. Jacob, “The correlation between physiological and psychological responses to odour stimulation in human subjects,” Clin Neurophysiol, vol. 113, no. 4, pp. 542-551, 2002.
[85] S. Wetter, and C. Murphy, “A paradigm for measuring the olfactory event-related potential in the clinic,” Int J Psychophysiol, vol. 49, no. 1, pp. 57-65, 2003.
[86] M. W. Geisler, and C. Murphy, “Event-related brain potentials to attended and ignored olfactory and trigeminal stimuli,” Int J Psychophysiol, vol. 37, no. 3, pp. 309-315, 2000.
[87] S. Nordin, M. Martinkauppi, J. Olofsson, T. Hummel, E. Millqvist, and M. Bende, “Chemosensory perception and event-related potentials in self-reported chemical hypersensitivity,” Int J Psychophysiol, vol. 55, no. 2, pp. 243-255, 2005.
[88] J. W. Covington, M. W. Geisler, J. Polich, and C. Murphy, “Normal aging and odor intensity effects on the olfactory event-related potential,” Int J Psychophysiol, vol. 32, no. 3, pp. 205-214, 1999.
[89] W. J. Evans, G. Kobal, T. S. Lorig, and J. D. Prah, “Suggestions for collection and reporting of chemosensory (olfactory) event-related potentials,” Chem Senses, vol. 18, no. 6, pp. 751-756, 1993.
[90] T. Hummel, S. Barz, E. Pauli, and G. Kobal, “Chemosensory event-related potentials change with age,” Electroencephalogr Clin Neurophysiol, vol. 108, no. 2, pp. 208-217, 1998.
[91] T. Hummel, T. Futschik, J. Frasnelli, and K.-B. Hüttenbrink, “Effects of olfactory function, age, and gender on trigeminally mediated sensations: a study based on the lateralization of chemosensory stimuli,” Toxicol Lett, vol. 140-141, pp. 273-280, 2003.
[92] J. N. Lundstrom, J. Frasnelli, M. Larsson, and T. Hummel, “Sex differentiated responses to intranasal trigeminal stimuli,” Int J Psychophysiol, vol. 57, no. 3, pp. 181-186, 2005.
[93] C. D. Morgan, M. W. Geisler, J. W. Covington, J. Polich, and C. Murphy, “Olfactory P3 in young and older adults,” Psychophysiology, vol. 36, no. 3, pp. 281-287, 1999.
[94] C. D. Morgan, J. W. Covington, M. W. Geisler, J. Polich, and C. Murphy, “Olfactory event-related potentials: older males demonstrate the greatest deficits,” Electroencephalogr Clin Neurophysiol, vol. 104, no. 4, pp. 351-358, 1997.
[95] B. M. Pause, B. Sojka, K. Krauel, G. Fehm-Wolfsdorf, and R. Ferstl, “Olfactory information processing during the course of the menstrual cycle,” Biol Psychol, vol. 44, no. 1, pp. 31-54, 1996.
[96] J. K. Olofsson, D. A. Broman, M. Wulff, M. Martinkauppi, and S. Nordin, “Olfactory and chemosomatosensory function in pregnant women assessed with event-related potentials,” Physiol Behav, vol. 86, no. 1-2, pp. 252-257, 2005.
[97] R. Masago, Y. Shimomura, K. Iwanaga, and T. Katsuura, “The Effects of Hedonic Properties of Odors and Attentional Modulation on the Olfactory Event-Related Potentials,” J Physiol Anthropol Appl Human Sci, vol. 20, no. 1, pp. 7-13, 2001.
[98] E. Caccappolo, H. Kipen, K. Kelly-McNeil, S. Knasko, R. M. Hamer, B. Natelson et al., “Odor perception: multiple chemical sensitivities, chronic fatigue, and asthma,” J Occup Environ Med, vol. 42, no. 6, pp. 629-638, 2000.
[99] A. Livermore, and T. Hummel, “The influence of training on chemosensory event-related potentials and interactions between the olfactory and trigeminal systems,” Chem Senses, vol. 29, no. 1, pp. 41-51, 2004.
[100] G. Barbati, R. Sigismondi, F. Zappasodi, C. Porcaro, S. Graziadio, G. Valente et al., “Functional source separation from magnetoencephalographic signals,” Hum Brain Mapp, vol. 27, no. 12, pp. 925-934, 2006.
[101] A. Belouchrani, K. A. Meraim, J. F. Cardoso, and E. Moulines, “A blind source separation technique using second order statistics,” IEEE Trans on Sig Proc, vol. 45, pp. 434-444, 1997.
[102] A. C. Tang, B. A. Pearlmutter, N. A. Malaszenko, and D. B. Phung, “Independent components of magnetoencephalography: single-trial response onset times,” Neuroimage, vol. 17, no. 4, pp. 1773-1789, 2002.
[103] K. H. Knuth, A. S. Shah, W. A. Truccolo, M. Ding, S. L. Bressler, and C. E. Schroeder, “Differentially variable component analysis: Identifying multiple evoked components using trial-to-trial variability,” J Neurophysiol, vol. 95, no. 5, pp. 3257-3276, 2006.
[104] S. D. Georgiadis, P. O. Ranta-aho, M. P. Tarvainen, and P. A. Karjalainen, “Single-trial dynamical estimation of event-related potentials: a Kalman filter-based approach,” IEEE Trans Biomed Eng, vol. 52, no. 8, pp. 1397-1406, 2005.
[105] R. Quian Quiroga, and H. Garcia, “Single-trial event-related potentials with wavelet denoising,” Clin Neurophysiol, vol. 114, no. 2, pp. 376-390, 2003.
[106] Z. Wang, A. Maier, D. A. Leopold, N. K. Logothetis, and H. Liang, “Single-trial evoked potential estimation using wavelets,” Comput Biol Med vol. 37, no. 4, pp. 463-473, 2007.
[107] E. Bingham, and A. Hyvarinen, “A fast fixed-point algorithm for independent component analysis of complex valued signals,” Int J Neural Syst, vol. 10, no. 1, pp. 1-8, 2000.
[108] S. Boesveldt, A. Haehner, H. Berendse, and T. Hummel, “Signal-to-noise ratio of chemosensory event-related potentials,” Clin Neurophysiol, vol. 118, no. 3, pp. 690-695, 2007.
[109] B. M. Battista, C. Knapp, T. McGee, and V. Goebel, “Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data,” Geophysics, vol. 72, no. 2, pp. H29-H37, 2007.
[110] H. Liang, S. L. Bressler, E. A. Buffalo, R. Desimone, and P. Fries, “Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention,” Biol Cybern, vol. 92, no. 6, pp. 380-392, 2005.
[111] T. Tateyama, T. Hummel, S. Roscher, H. Post, and G. Kobal, “Relation of olfactory event-related potentials to changes in stimulus concentration,” Electroencephalogr Clin Neurophysiol, vol. 108, no. 5, pp. 449-455, 1998.
[112] B. M. Pause, K. Krauel, B. Sojka, and R. Ferstl, “Is odor processing related to oral breathing?,” Int J Psychophysiol, vol. 32, no. 3, pp. 251-260, 1999.
[113] F. Cong, T. Sipola, T. Huttunen-Scott, X. Xu, T. Ristaniemi, and H. Lyytinen, “Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm,” Nonlinear Biomed Phys, vol. 3, no. 1, pp. 1, 2009.
[114] N. Williams, S. J. Nasuto, and J. D. Saddy, “Evaluation of Empirical Mode Decomposition for Event-Related Potential Analysis,” EURASIP JASP, vol. 2011, 2011.
[115] T. Solis-Escalante, G. G. Gentiletti, and O. Yanez-Suarez, “Single trial P300 detection based on the Empirical Mode Decomposition,” Conf Proc IEEE Eng Med Biol Soc, vol. 1, pp. 1157-1160, 2006.
[116] N. ur Rehman, and D. P. Mandic, “Filter Bank Property of Multivariate Empirical Mode Decomposition,” IEEE Trans Sig Proc, vol. 59, no. 5, pp. 2421-2426, 2011.
[117] A. Y. Mutlu, and S. Aviyente, “Multivariate empirical mode decomposition for quantifying multivariate phase synchronization,” EURASIP J Adv Sig Pr, vol. 2011, pp. 1-13, 2011.
[118] J. Fleureau, A. Kachenoura, L. Albera, J.-C. Nunes, and L. Senhadji, “Multivariate empirical mode decomposition and application to multichannel filtering,” Signal Processing, vol. 91, no. 12, pp. 2783-2792, 2011.
[119] M. J. Kaminski, and K. J. Blinowska, “A new method of the description of the information flow in the brain structures,” Biol Cybern, vol. 65, no. 3, pp. 203-210, 1991.
[120] B. Kocsis, and M. Kaminski, “Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system,” Hippocampus, vol. 16, no. 6, pp. 531-540, 2006.
[121] L. A. Baccala, and K. Sameshima, “Partial directed coherence: a new concept in neural structure determination,” Biol Cybern, vol. 84, no. 6, pp. 463-474, 2001.
[122] E. Florin, J. Gross, J. Pfeifer, G. R. Fink, and L. Timmermann, “The effect of filtering on Granger causality based multivariate causality measures,” Neuroimage, vol. 50, no. 2, pp. 577-588, 2010.