使用全息希爾伯特頻譜分析法解訊非線性及非穩態的大腦神經活動-以穩態視覺誘發電位為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：33

、訪客IP：3.142.197.212

姓名

阮鐘堅(Nguyen Trong Kien) 查詢紙本館藏

畢業系所

認知與神經科學研究所

論文名稱

使用全息希爾伯特頻譜分析法解訊非線性及非穩態的大腦神經活動-以穩態視覺誘發電位為例
(Using Holo-Hilbert Spectral Analysis to decipher nonlinear and nonstationary neural activities: an example from steady state visual evoked potentials)

相關論文

★ 時間及空間對注意力暫失的影響以及其可能的神經生理機制	★ 注意力分配及眼球運動準備歷程對於眼動潛伏時間與眼動軌跡的影響
★ 注意力暫失中的數字表徵: 數字距離對注意力暫失的影響	★ 利用跨顱磁刺激探討主動式注意力攫取的神經機制
★ 以數學模型及跨顱磁刺激探討注意力分配及眼球運動準備歷程	★ 學齡前兒童之視覺注意力發展及電腦化注意力訓練效果之探討
★ 以跨顱磁刺激探討左側下部頂葉以及左側上部頂葉的功能在中文處理中所扮演的角色	★ 性侵害犯的衝動行為表現-情緒狀態如何影響性侵害犯的抑制能力？
★ 學齡前階段孩童眼動抑制能力的發展和特性	★ 學齡前階段孩童衝突解決和動作反應抑制能力的發展
★ 6歲孩童與成人在數字和具體數量上的自動化處理	★ 期望效果之影響與可能的神經機制
★ Attentional reorienting: the dynamic interaction between goal-directed and stimulus-driven attentioinal control	★ 前額葉眼動區在視覺搜尋作業上對不同干擾物特徵與顯示時間扮演的角色
★ Roles of the Pre-supplementary Motor Area and Right Inferior Frontal Gyrus in Stimulus Selective Stop-signal task: A Theta Burst Transcranial!Magnetic! Stimulation!Study	★ Investigation of posterior parietal cortex visuospatial control over processing in near and far space using transcranial magnetic stimulation

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

自然界的感覺訊號存在非線性的結構，由訊號的載波頻率及強度的動態變化組成。目前已有許多神經造影及電生理的研究顯示刺激的訊號包絡（signal envelope）對於生存及知覺的重要性。然而，目前對於人類大腦如何處理包絡訊息仍未有完整的暸解，其中最主要的原因是傳統的分析技術未能精確地量化這些訊息。因此，本論文提出新穎的資料分析法，透過分析穩態視覺誘發電位（steady-state visually evoked potential, SSVEP）的訊號包絡來探討大腦訊息處理的神經機制。SSVEP具有穩定及容易測量的特性，因此相當適合用來研究大腦功能的能量頻譜及反應延遲（response latency）。本論文使用發光二極體產生簡單及複雜的視覺刺激來誘發SSVEP，並使用以經驗模態分解（empirical mode decomposition, EMD）為基礎的全息希爾伯特頻譜分析法（Holo-Hilbert spectral analysis, HHSA）來分析不同刺激誘發電位的線性及非線性頻譜特徵。以EMD擷取的訊號包絡為基礎，我進一步發展了包絡相位延遲法來測量從視覺刺激誘發的反應延遲。HHSA的分析結果顯示，複雜LED刺激誘發的SSVEP能夠反映刺激本身的訊號特徵（2赫茲包絡調控14赫茲載波）。除此之外，我亦從HHSA中發現視覺系統對LED刺激的加工：SSVEP的14赫茲載波被4赫茲包絡所調控，同時2赫茲包絡調控的載波頻率涵蓋了從8赫茲到32赫茲的頻段；本研究是第一個報告此現象的文獻。刺激包絡明顯的調控了對於即將進入之訊號的反應。HHSA更有助於比較跨頻率的交互作用及傳統的相位振幅耦合方法。除了能量頻譜外，利用包絡相位延遲法計算從LED刺激呈現到Oz電極記錄到相關電位的反應延遲約為104.55毫秒；在單眼刺激的情況下，優勢眼的反應延遲約為97.14毫秒，而弱勢眼的反應延遲約為104.75毫秒，但兩者差異未達到統計顯著。整體而言，本論文奠定了將HHSA應用於研究大腦活動訊號之非線性特徵及跨頻率交互作用的具體基礎，同時也提供一套嶄新的方法，可用以測量不同腦區間的資訊傳遞.

摘要(英)

Natural sensory signals have nonlinear structures dynamically composed of the carrier frequencies and the variation of the amplitude (i.e., envelope). Neuroimaging and electrophysiological studies have demonstrated the envelope stimulus is vital for survival and perception. However, how the human brain processes the envelope information is still poorly understood. It is largely due to the conventional analysis failing to quantify it directly. Hence, this dissertation proposed the novel methods to investigate the underlying neural mechanism of envelope responses collected by Steady-state visually evoked potentials (SSVEPs), which offer reliable and quantitative data to investigate the function of the human brain based on the power spectrum and response latency. The Holo-Hilbert spectral analysis (HHSA), which is a nonlinear analysis tool based on the Empirical Mode Decomposition (EMD), was used to investigate power spectrum of the fundamental and nonlinear features while the envelope-based phase delay method was developed to measure the visual latency. The results of HHSA demonstrate that in addition to the 2 Hz fundamental envelope, 4 Hz amplitude modulation residing in 14 Hz carrier and a broad range of carrier frequencies covering from 8 to 32 Hz modulated by 2 Hz amplitude modulation are also found in the two-dimensional frequency spectrum, which have not yet been recognized before. The envelope of the stimulus is also found to dominantly modulate the response to the incoming signal. Furthermore, the HHSA was also beneficial to investigate the cross-frequency interaction compared to the traditional Phase-amplitude coupling method. Besides the power spectrum, the envelope-based phase delay method shows that the response latency at the occipital lobe (Oz channel) was approximately 104.55 ms for binocular stimulation, and 97.14 ms for the dominant eye and 104.75 ms for the non-dominant eye with no significant difference of response latency between these stimulations. In sum, these findings offer the novel methods for future studies in quantifying the nonlinear features, cross-frequency interaction, and also potentially shed new light on understanding of how long collective neural activities take to travel in the human brain

關鍵字(中)

★ 穩態視覺誘發電位

關鍵字(英)

★ Steady-state visually evoked potential

論文目次

中文摘要 i
Abstract iii
List of Tables ix
List of Figures x
Chapter 1 : General introduction 1
1.1 Overview 1
1.2. Electroencephalogram (EEG) 4
1.2.1. Overview 4
1.2.2. Visual evoked potentials (VEPs) 6
1.2.3. Steady-state visually evoked potentials 7
1.3. SSVEP and nonlinear processes in the visual system 8
1.4. Analysis of nonstationary and nonlinear signals 11
1.4.1 Event-related Mode (ERM) 11
1.4.2. Fast Fourier transform (FFT) 13
1.4.3. Hilbert Huang transform (HHT) 14
1.4.4. Holo-Hilbert Spectral Analysis (HHSA) 15
1.4.5. Phase amplitude coupling (PAC) 18
1.5. Measuring the visual response latency in visual system 20
1.6. Purpose and hypothesis 22
Chapter 2 : Unraveling nonlinear electrophysiological processes in the human visual system with full dimension spectral analysis 25
2.1. Introduction 25
2.2. Materials and Methods 28
2.2.1. Participants. 28
2.2.2. Stimuli and Procedures. 28
2.2.3. EEG data acquisition and preprocessing. 33
2.2.4. Holo-Hilbert spectral analysis. 33
2.2.5. Steady state visually evoked potential analysis. 35
2.2.6. Statistical analysis. 39
2.3. Results 40
2.3.1. Experiment 1: Monocular and binocular stimulation. 40
2.3.2. The amplitude spectrum of SSVEP responses: FFT and HHSA results. 48
2.3.3. Occipital activity induced by the envelope of AM flicker. 56
2.3.4. Experiment 2: Dichoptic stimulation. 58
2.4. Discussion 63
Chapter 3 : Revealing the nature of amplitude modulated neural entrainment with Holo-Hilbert Spectral Analysis. 70
3.1. Introduction 70
3.2. Materials and Methods 75
3.2.1. Phase amplitude coupling (PAC) 75
3.2.2. Experimental data 78
3.2.3. EEG data acquisition and preprocessing 81
3.3. Results 82
3.3.1. Simulations 82
3.3.2. SSVEP results 93
3.4. Discussion 106
Chapter 4 : Human visual steady-state responses to amplitude-modulated flicker: Latency measurement 115
4.1 Introduction 115
4.2. Materials and Methods 119
4.2.1. Data set: 119
4.2.2. EEG data acquisition and preprocessing: 119
4.2.3. Latency estimation based on the envelope response 120
4.2.4. Statistical analysis 127
4.2.5. Source Localization analysis 127
4.3. Results 128
4.3.1. The propagating time (group delay) from the stimulus presentation to the occipital channel (Oz) during binocular and monocular stimulation. 128
4.3.2. The propagating time (group delay) for the frontal lobe channels was longer than for the occipital channels during binocular stimulation 132
4.4. Discussion 137
Chapter 5 : General discussion 144
5.1 Results summary 144
5.2 Conclusion 145
5.3 Limitation and future directions 147
References 157
Appendices 177

參考文獻

Abreu, T., Silva, P. A., Sancho, F., & Temperville, A. (2010). Analytical approximate wave form for asymmetric waves. Coastal Engineering, 57(7), 656–667.
Adrian, E. D., & Matthews, B. H. C. (1934a). The berger rhythm: Potential changes from the occipital lobes in man. Brain, 57(4), 355–385.
Adrian, E. D., & Matthews, B. H. C. (1934b). The berger rhythm: Potential changes from the occipital lobes in man. Brain, 57(4), 355–385.
Andersen, S. K., Hillyard, S. A., & Muller, M. M. (2013). Global Facilitation of Attended Features Is Obligatory and Restricts Divided Attention. Journal of Neuroscience, 33(46), 18200–18207.
Andersen, S. K., & Müller, M. M. (2015). Driving steady-state visual evoked potentials at arbitrary frequencies using temporal interpolation of stimulus presentation. BMC Neuroscience, 16(1), 95.
Apkarian, P. A., Nakayama, K., & Tyler, C. W. (1981). Binocularity in the human visual evoked potential: Facilitation, summation and suppression. Electroencephalography and Clinical Neurophysiology, 51(1), 32–48.
Apostolidis, G. K. & Hadjileontiadis, L. J. Swarm decomposition: A novel signal analysis using swarm intelligence. Signal Processing 132, 40–50 (2017).
Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., … Vicente, R. (2015). Untangling cross-frequency coupling in neuroscience. Current Opinion in Neurobiology.
Azzopardi, P., Fallah, M., Gross, C. G., & Rodman, H. R. (2003). Response latencies of neurons in visual areas MT and MST of monkeys with striate cortex lesions. Neuropsychologia, 41(13), 1738–1756.
Baitch, L. W., & Levi, D. M. (1988). Evidence for nonlinear binocular interactions in human visual cortex. Vision Research, 28(10), 1139–1143. https://doi.org/10.1016/0042-6989(88)90140-X
Baitch, L. W., & Levi, D. M. (1989). Binocular beats: Psychophysical studies of binocular interaction in normal and stereoblind humans. Vision Research, 29(1), 27–35.
Baker, C. L., & Mareschal, I. (2001). Processing of second-order stimuli in the visual cortex, 134, 1–21.
Baker, D. H., Lygo, F. A., Meese, T. S., & Georgeson, M. A. (2018). Binocular Summation Revisited : Beyond ͌ 2. Psychological Bulletin, 144(11), 1186–1199.
Baker, D. H., Wallis, S. A., Georgeson, M. A., & Meese, T. S. (2012). Nonlinearities in the binocular combination of luminance and contrast. Vision Research, 56, 1–9.
Belluscio, M. A., Mizuseki, K., Schmidt, R., Kempter, R., & Buzsáki, G. (2012). Cross-Frequency Phase–Phase Coupling between Theta and Gamma Oscillations in the Hippocampus. The Journal of Neuroscience, 32(2), 423 LP-435.
Benda, J., Maler, L., & Middleton, J. W. (2006). The cellular basis for parallel neural transmission of a high-frequency stimulus and its low-frequency envelope, 103(39).
Berman, J. I., Mcdaniel, J., Liu, S., Cornew, L., Gaetz, W., Roberts, T. P. L., & Edgar, J. C. (2012). Variable Bandwidth Filtering for Improved Sensitivity of Cross-Frequency Coupling Metrics, 2(3), 155–163.
Bianciardi, M., Bianchi, L., Garreffa, G., Abbafati, M., Di Russo, F., Marciani, M. G., & Macaluso, E. (2009). Single-epoch analysis of interleaved evoked potentials and fMRI responses during steady-state visual stimulation. Clinical Neurophysiology, 120(4), 738–747.
Bieser, A., & Müller-Preuss, P. (1996). Auditory responsive cortex in the squirrel monkey: neural responses to amplitude-modulated sounds. Experimantal Brain Research, 108(2), 273–284.
Bijl, G. K., & Veringa, F. (1985). Neural conduction time and steady-state evoked potentials. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 62(6), 465–467.
Blake, R. (1989). A Neural Theory of Binocular Rivalry. Psychological Review, 96(1), 145–167.
Blake, R., Westendorf, D., & Fox, R. (1990). Temporal perturbations of binocular rivalry. Perception & Psychophysics, 48(6), 593–602.
Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20(3), 340–352.
Brown, R. J., & Norcia, A. M. (1997). A method for investigating binocular rivalry in real-time with the steady-state VEP. Vision Research, 37(17), 2401–2408.
Buzsáki, G., & Mizuseki, K. (2014). The log-dynamic brain: how skewed distributions affect network operations. Nature Reviews. Neuroscience, 15(4), 264–278.
Campbell, F. W., & Green, D. G. (1965). Monocular versus Binocular Visual Acuity. Nature, 208(5006), 191–192.
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., … Knight, R. T. (2006). High gamma power is phase-locked to theta oscillations in human neocortex. Science, 313(5793), 1626–1628.
Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences, 14(11), 506–515.
Canolty, R. T., & Knight, R. T. (2010). The functional role of cross-frequency coupling. Trends in Cognitive Sciences.
Celesia, G. G. (1980). Steady-state and transient visual evoked potentials in clinical practice. Annals of the New York Academy of Sciences, 338, 290–305.
Chang, K. M. (2010). Ensemble empirical mode decomposition for high frequency ECG noise reduction. World Scientific, 55(4), 193–201.
Chubb, C., & Sperling, G. (1988). Drift-balanced random stimuli: A general basis for studying non-Fourier motion perception. Journal of the Optical Society of America. A, Optics and Image Science, 5(11), 1986–2007.
Clarke, S. E., Longtin, A., & Maler, L. (2015). Contrast coding in the electrosensory system: Parallels with visual computation. Nature Reviews Neuroscience.
Cohen, M. X. (2008). Assessing transient cross-frequency coupling in EEG data, 168, 494–499.
Cole, S. R., van der Meij, R., Peterson, E. J., de Hemptinne, C., Starr, P. A., & Voytek, B. (2017). Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson′s Disease. The Journal of Neuroscience, 37(18), 4830 LP-4840
Cole, S. R., & Voytek, B. (2017). Brain Oscillations and the Importance of Waveform Shape. Trends in Cognitive Sciences.
Cole, S., & Voytek, B. (2016). Brain oscillations and the importance of waveform shape. Trends in Cognitive Sciences, xx, 1–13.
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18(2), 192—205.
Cowey, A. (1964). Projection of the retina on to striate and prestriate cortex in the squirrel monkey, Saimiri sciureus. Journal of Neurophysiology, 27, 366–393 .
De Lange, H. (1958). Research into the Dynamic Nature of the Human Fovea: Cortex Systems with Intermittent and Modulated Light I. Attenuation Characteristics with White and Colored Light. Journal of the Optical Society of America, 48(11), 777.
Deering, R., & Kaiser, J. F. (2005). The use of a masking signal to improve empirical mode decomposition. In Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. (Vol. 4, p. iv/485-iv/488 Vol. 4).
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.
Demb, J. B., Zaghloul, K., & Sterling, P. (2001). Cellular basis for the response to second-order motion cues in Y retinal ganglion cells. Neuron, 32(4), 711–721.
Di Russo, F., Pitzalis, S., Aprile, T., Spitoni, G., Patria, F., Stella, A., … Hillyard, S. A. (2007). Spatiotemporal analysis of the cortical sources of the steady-state visual evoked potential. Human Brain Mapping, 28(4), 323–334.
Di Summa, A., Polo, A., Tinazzi, M., Zanette, G., Bertolasi, L., Bongiovanni, L. G., & Fiaschi, A. (1997). Binocular interaction in normal vision studied by pattern-reversal visual evoked potential (PR-VEPS). Italian Journal of Neurological Sciences, 18(2), 81–86.
Ding, J., & Levi, D. M. (2017). Binocular combination of luminance profiles. Journal of Vision, 17(13), 1–32.
Ding, J., & Sperling, G. (2006). A gain-control theory of binocular combination, 103(4), 1141–1146.
Draganova, R., & Popivanov, D. (1999). Assessment of EEG frequency dynamics using complex demodulation. Physiological Research.
Falsini, B., & Porciatti, V. (1996). The temporal frequency response function of pattern ERG and VEP: changes in optic neuritis. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 100(5), 428–435.
Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex (New York, N.Y. : 1991), 1(1), 1—47.
Fisher, N. I. (1993). Statistical Analysis of Circular Data. Book.
Freeman, W. J., & Burke, B. C. (2003). A neurobiological theory of meaning in perception. Part 4. Multicortical patterns of amplitude modulation in gamma EEG. International Journal of Bifurcation and Chaos, 13, 2857–2866.
Freeman, W. J., & Vitiello, G. (2006). Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics. Physics of Life Reviews.
Freeman, W. J., & Vitiello, G. (2015). Matter and Mind are Entangled in EEG Amplitude Modulation and its Double.
Fries P. (2015). Rhythms for Cognition: Communication through Coherence. Neuron, 88(1), 220–235.
Fleming, G. (1938). The Electro-encephalogram in Diagnosis and in Localization of Epileptic Seizures. (Arch. Neur. and Psychiat., vol. xxxvi, p. 1225, December, 1936.)
Gibbs, F. A., Lennox, W. G., and Gibbs, E. L. Journal of Mental Science, 84(348), 233-233.
Goldstein, J., Baer, T., & Kiang, N. (1971). A Theoretical Treatment of Latency, Group Delay, and Tuning Characteristics for Auditory-Nerve Responses to Clicks and Tones. In Physiology of the Auditory System (pp. 133–141).
Grahn, J. A. (2012). See what I hear? Beat perception in auditory and visual rhythms. Experimental Brain Research, 220(1), 51–61.
Grinvald, A., Lieke, E. E., Frostig, R. D., & Hildesheim, R. (1994). Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. The Journal of Neuroscience, 14(5), 2545 LP-2568.
Hilgetag, C.-C., Van Essen, D. C., Hilgetag, C.-C., Van Essen, D. C., ONeill, M. A., Young, M. P., … Felleman, D. J. (1996). Indeterminate Organization of the Visual System. Science, 271(5250), 776.
Honing, H., Bouwer, F. L., & Háden, G. P. (2014). Perceiving temporal regularity in music: The role of auditory Event-Related Potentials (ERPs) in probing beat perception. Advances in Experimental Medicine and Biology, 829, 305–323.
Horwitz, G. D., & Hass, C. A. (2012). Nonlinear analysis of macaque V1 color tuning reveals cardinal directions for cortical color processing. Nature Neuroscience, 15(6), 913–919.
Hsu, C. H., Lee, C. Y., & Liang, W. K. (2016). An improved method for measuring mismatch negativity using ensemble empirical mode decomposition. J Neurosci Methods, 264, 78–85.
Hsu, T. Y., Tseng, P., Liang, W. K., Cheng, S. K., & Juan, C. H. (2014). Transcranial direct current stimulation over right posterior parietal cortex changes prestimulus alpha oscillation in visual short-term memory task. NeuroImage, 98, 306–313.
Huang, N. E., Hu, K., Yang, A. C. C., Chang, H.-C., Jia, D., Liang, W.-K., … Wu, Z. (2016). On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150206.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in Adaptive Data Analysis, 01(02), 177–229.
Huang, N. E., Young, V., Lo, M., Wang, Y. H., Peng, C. K., Chen, X., … Wu, Z. (2013). The uniqueness of the instantaneous frequency based on instrinsci mode function. Advances in Adaptive Data Analysis, 05(03), 1350011.
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154.
Hyafil, A. (2015). Misidentifications of specific forms of cross-frequency coupling: Three warnings. Frontiers in Neuroscience, 9(OCT), 1–6.
Iatsenko, D., McClintock, P.V.E., Stefanovska, A., 2015. Nonlinear mode decomposition: A noise-robust, adaptive decomposition method. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 92, 1–25.
Jasper, H. H., & Carmichael, L. (1935). Electrical Potentials from the Intact Human Brain. Science, 81(2089), 51–53.
Jensen, O., & Colgin, L. L. (2007). Cross-frequency coupling between neuronal oscillations. Trends in Cognitive Sciences, 11(7), 267–269.
Jensen, O., Spaak, E., & Park, H. (2016). Discriminating Valid from Spurious Indices of Phase-Amplitude Coupling. ENeuro, 3(6), ENEURO.0334-16.2016.
Jiang, H., Bahramisharif, A., Gerven, M. A. J. Van, & Jensen, O. (2015). NeuroImage Measuring directionality between neuronal oscillations of different frequencies. NeuroImage, 118, 359–367.
Johansson, B., & Jakobsson, P. (1993). VEP latency : a comparison between normal and defective binocularity. Clinical Vision Sciences, 8, 245–251.
Johnson, A. P., & Baker, C. L. (2004). First- and second-order information in natural images: a filter-based approach to image statistics. Journal of the Optical Society of America A, 21(6), 913.
Kamp, A., Sem-Jacobsen, C. W., van Leeuwen, W. S., van der T-Weel, L. H., Sem‐Jacobsen, C. W., van Leeuwen, W. S., & der T‐Weel, L. H. va. (1960). Cortical Responses to Modulated Light in the Human Subject. Acta Physiologica Scandinavica, 48(1), 1–12.
Kashiwase, Y., Matsumiya, K., Kuriki, I., & Shioiri, S. (2012). Time Courses of Attentional Modulation in Neural Amplification and Synchronization Measured with Steady-state Visual-evoked Potentials. Journal of Cognitive Neuroscience, 24(8), 1779–1793.
Kelly, D. H. (1961). Visual Responses to Time-Dependent Stimuli* I Amplitude Sensitivity Measurements†. Journal of the Optical Society of America, 51(4), 422.
Kelly, D. H. (1971). Theory of flicker and transient responses. I. Uniform fields. Journal of the Optical Society of America, 61(4), 537–546.
Kitano, M., Niiyama, K., Kasamatsu, T., Sutter, E. E., & Norcia, A. M. (1994). Retinotopic and nonretinotopic field potentials in cat visual cortex. Visual Neuroscience, 11(5), 953–977.
Kohlrausch, A., Fassel, R., & Dau, T. (2000). The influence of carrier level and frequency on modulation and beat-detection thresholds for sinusoidal carriers. The Journal of the Acoustical Society of America, 108(2), 723–734.
Kovács, I., & Fehér, Á. (1997). Non-Fourier information in bandpass noise patterns. Vision Research, 37(9), 1167–1175.
Le Van Quyen, M., Foucher, J., Lachaux, J., Rodriguez, E., Lutz, a, Martinerie, J., & Varela, F. J. (2001). Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. Journal of Neuroscience Methods, 111(2), 83–98.
Lee, J., Birtles, D., Wattam-Bell, J., Atkinson, J., & Braddick, O. (2012). Latency measures of pattern-reversal VEP in adults and infants: Different information from transient P1 response and steady-state phase. Investigative Ophthalmology and Visual Science, 53(3), 1306–1314.
Lennerstrand, G. (1978). Binocular interaction studied with visual evoked responses in human with normal or impaired binocular vision. Acta Ophthalmol (Copenh), 56, 628–637.
Lennie, P. (1981). The physiological basis of variations in visual latency. Vision Research, 21(6), 815–824.
Liang, H., Bressler, S. L., Desimone, R., & Fries, P. (2005). Empirical mode decomposition: A method for analyzing neural data. Neurocomputing, 65–66(SPEC. ISS.), 801–807.
Liégeois-Chauvel, C., Lorenzi, C., Trébuchon, A., Régis, J., & Chauvel, P. (2004). Temporal envelope processing in the human left and right auditory cortices. Cerebral Cortex, 14(7), 731–740.
Lozano-Soldevilla, D., ter Huurne, N., & Oostenveld, R. (2016). Neuronal Oscillations with Non-sinusoidal Morphology Produce Spurious Phase-to-Amplitude Coupling and Directionality. Frontiers in Computational Neuroscience, 10(August), Article 87.
Mancini, F., Pepe, A., Bernacchia, A., Di Stefano, G., Mouraux, A., & Iannetti, G. D. (2018). Characterizing the short-term habituation of event-related evoked potentials. ENeuro, 5(5), 1–14.
Mareschal, I., & Baker, C. L. (1998). Temporal and spatial response to second-order stimuli in cat area 18. Journal of Neurophysiology, 80(6), 2811–2823.
Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177–190.
Martinez-cancino, R., Heng, J., Delorme, A., Kreutz-delgado, K., Sotero, R. C., & Makeig, S. (2018). Measuring transient phase-amplitude coupling using local mutual information. NeuroImage.
Maunsell, J. H., & Gibson, J. R. (1992). Visual response latencies in striate cortex of the macaque monkey. Journal of Neurophysiology, 68(4), 1332–1344.
Mcculloch, D. L., & Skarf, B. (1991). Development of the Human Visual System : Monocular and Binocular Pattern VEP Latency, 32(8).
McKerral, M., Lachapelle, P., Tremblay, F., Polomeno, R. C., Roy, M. S., Beneish, R., & Lepore, F. (1996). Monocular contribution to the peak time of the binocular pattern-visual evoked potential, 181–193.
Meese, T. S., Georgeson, M. A., & Baker, D. H. (2006). Binocular contrast vision at and above threshold. Journal of Vision, 6(11), 7.
Merigan, W. H., & Maunsell, J. H. R. (1993). How Parallel are the Primate Visual Pathways? Annual Review of Neuroscience, 16(1), 369–402. https://doi.org/10.1146/annurev.ne.16.030193.002101
Middleton, Longtin, Benda, & Maler. (2006). The cellular basis for parallel neural transmission of a high-frequency stimulus and its low-frequency envelope. Proceedings Of The National Academy Of Sciences Of The United States, USA, 103(39), 14596–14601.
Miles, W. R. (1930). Ocular Dominance in Human Adults. The Journal of General Psychology, 3(3), 412–430.
Morrone, M. C., Fiorentini, A., & Burr, D. C. (1996). Development of the temporal properties of visual evoked potentials to luminance and colour contrast in infants. Vision Research, 36(19), 3141–3155.
Muller, L., Reynaud, A., Chavane, F., & Destexhe, A. (2014). The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave. Nature Communications, 5, 3675.
Müller, M. M., Andersen, S. K., & Keil, A. (2008). Time course of competition for visual processing resources between emotional pictures and foreground task. Cerebral Cortex, 18(8), 1892–1899.
Muller, M. M., Andersen, S., Trujillo, N. J., Valdes-Sosa, P., Malinowski, P., & Hillyard, S. A. (2006). Feature-selective attention enhances color signals in early visual areas of the human brain. Proceedings of the National Academy of Sciences, 103(38), 14250–14254.
Müller, M. M., Teder, W., & Hillyard, S. a. (1997). Magnetoencephalographic recording of steady-state visual evoked cortical activity. Brain Topography, 9(3), 163–168.
Nguyen, K. T., Liang, W.-K., Lee, V., Chang, W.-S., Muggleton, N. G., Yeh, J.-R., … Juan, C.-H. (2019). Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis. Scientific Reports, 9(1), 16919.
Nichols, T. E., & Holmes, A. P. (2001). Nonparametric Permutation Tests For Functional Neuroimaging : A Primer with Examples, 25(August 1999), 1–25.
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015a). The steady-state visual evoked potential in vision research: A review. Journal of Vision, 15(6), 4.
Novembre, G., & Iannetti, G. D. (2018). Tagging the musical beat: Neural entrainment or event-related potentials? Proceedings of the National Academy of Sciences of the United States of America, 115(47), E11002–E11003
Nowak, L. G., Munk, M. H., Girard, P., & Bullier, J. (1995). Visual latencies in areas V1 and V2 of the macaque monkey. Visual Neuroscience, 12(2), 371–384.
Nunez, P. L., & Srinivasan, R. (2009). Electric Fields of the Brain: The neurophysics of EEG. Electric Fields of the Brain: The Neurophysics of EEG, 59, 1–611.
Odom, J. V., Bach, M., Brigell, M., Holder, G. E., McCulloch, D. L., Mizota, A., & Tormene, A. P. (2016). ISCEV standard for clinical visual evoked potentials: (2016 update). Documenta Ophthalmologica, 133(1).
Odom, J. V., & Chao, G. M. (1995). Models of binocular luminance interaction evaluated using visually evoked potential and psychophysical measures: A tribute to M. Russell harter. International Journal of Neuroscience, 80(1–4), 255–280.
Okamoto, Y., Nakagawa, S., Fujii, K., & Yano, T. (2009). Visual sensitivity and cortical response to the temporal envelope of amplitude-modulated flicker. Journal of the Optical Society of America A, 26(11), 2346–2352.
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Intell. Neuroscience, 2011, 1:1--1:9.
Pascual-Marqui, R. D. (2002). Standardized low resolution brain electromagnetic tomography (. Methods and Findings in Experimental and Clinical Pharmacology, 24 Suppl D, 5–12.
Perlstein, W. M., Cole, M. A., Larson, M., Kelly, K., Seignourel, P., & Keil, A. (2003). Steady-state visual evoked potentials reveal frontally-mediated working memory activity in humans. Neuroscience Letters, 342(3), 191–195. https://doi.org/https://doi.org/10.1016/S0304-3940(03)00226-X
Plainis, S., Petratou, D., Giannakopoulou, T., & Atchison, D. A. (2010). Binocular Summation Improves Performance to Defocus-Induced Blur. Visual Psychophysics and Physiological Optics, 2784–2789.
Porciatti, V., Burr, D. C., Morrone, M. C., & Fiorentini, A. (1992). The effects of ageing on the pattern electroretinogram and visual evoked potential in humans. Vision Research, 32(7), 1199–1209.
Porciatti, V., & Sartucci, F. (1996). Retinal and cortical evoked responses to chromatic contrast stimuli Specific losses in both eyes of patients with multiple sclerosis and unilateral optic neuritis, 723–740.
Ratliff, F., & Zemon, V. (1982). Some new methods for the analysis of lateral interactions that influence the visual evoked potential. Annals of the New York Academy of Sciences, 388(1), 113–124.
Regan, D. (1966a). Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology, 20(3), 238–248.
Regan, D. (1966b). Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalography and Clinical Neurophysiology, 20(3), 238–248.
Regan, D. (1976). Latencies of evoked potentials to flicker and to pattern speedily estimated by simultaneous stimulation method. Electroencephalography and Clinical Neurophysiology, 40(6), 654–660.
Regan, D. (1982). Comparison of transient and steady-state methods. Annals of the New York Academy of Sciences, 388, 45–71 .
Regan, D. (1983). Spatial frequency mechanisms in human vision investigated by evoked potential recording. Vision Research, 23(12), 1401–1407.
Regan, D. (1989). Human brain electrophysiology. Evoked potentials and evoked magnetic fields in science and medicine. Electroencephalography and Clinical Neurophysiology, 73(1), 84.
Regan, D., & Heron, J. R. (1969). Clinical investigation of lesions of the visual pathway: a new objective technique. Journal of Neurology, Neurosurgery, and Psychiatry, 32(5), 479–483.
Regan, D., & Regan, M. P. (1988). Objective evidence for phase-independent spatial frequency analysis in the human visual pathway. Vision Research, 28(1), 187–191.
Regan, M. P., & Regan, D. (1989). Objective Investigation of Visual Function Using a Nondestructive Zoom-FFT Technique for Evoked Potential Analysis. Canadian Journal of Neurological Sciences / Journal Canadien Des Sciences Neurologiques, 16(2), 168–179.
Richard, B., Chadnova, E., & Baker, D. H. (2018). NeuroImage Binocular vision adaptively suppresses delayed monocular signals. NeuroImage, 172(November 2017), 753–765.
Roach, B. J., & Mathalon, D. H. (2008). Event-related EEG time-frequency analysis: An overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophrenia Bulletin.
Rosenberg, A., Husson, T. R., & Issa, N. P. (2010). Subcortical Representation of Non-Fourier Image Features. The Journal of Neuroscience, 30(6), 1985 LP-1993. Retrieved from
Rosenberg, A., & Issa, N. P. (2011). The Y Cell Visual Pathway Implements a Demodulating Nonlinearity. Neuron, 71(2), 348–361.
Russell Harter, M., Towle, V. L., Zakrzewski, M., & Moyer, S. M. (1977). An objective indicant of binocular vision in humans: Size-specific interocular suppression of visual evoked potentials. Clinical Neurophysiology, 43(6), 825–836.
Samiee, S., & Baillet, S. (2017). NeuroImage Time-resolved phase-amplitude coupling in neural oscillations. NeuroImage, 159(February), 270–279.
Sato, E., Taniai, M., Mizota, A., & Adachi-Usami, E. (2002). Binocular interaction reflected in visually evoked cortical potentials as studied with pseudorandom stimuli. Investigative Ophthalmology and Visual Science, 43(10), 3355–3358.
Schmolesky, M. T., Wang, Y., Hanes, D., Thompson, K. G., Leutgeb, S., Schall, J. D., & Leventhal, a G. (1998). Signal timing across the macaque visual system. Jnp, 79(6), 3272–3278.
Schroeder, C. E., Tenke, C. E., Arezzo, J. C., & Vaughan, H. G. (1989). Timing and distribution of flash-evoked activity in the lateral geniculate nucleus of the alert monkey. Brain Research, 477(1–2), 183–195.
Schwartz, O., & Simoncelli, E. P. (2001). Natural signal statistics and sensory gain control. Nature Neuroscience, 4(8), 819–825.
Shapley, R. (1998). Visual cortex: pushing the envelope. Nature Neuroscience, 1(2), 95–96.
Shapley, R., & Hugh Perry, V. (1986). Cat and monkey retinal ganglion cells and their visual functional roles. Trends in Neurosciences, 9, 229–235.
Sharon, D., Jancke, D., Chavane, F., Na’aman, S., & Grinvald, A. (2007). Cortical Response Field Dynamics in Cat Visual Cortex. Cerebral Cortex, 17(12), 2866–2877.
Silberstein RB. (1995). Steady-state visually evoked potentials, brain resonances, and cognitive processes. In: Nunez PL, Editor. Neocortical Dynamics and Human EEG Rhythms., Oxford: Ox, 272–303.
Spekreijse, H. (1969). Rectification in the goldfish retina: Analysis by sinusoidal and auxiliary stimulation. Vision Research, 9(12), 1461–1472.
Spekreijse, H., & Reits, D. (1980). Sequential analysis of the visual evoked potential in man: Nonlinear analysis of a sandwich system. Annals of the New York Academy of Sciences, 338(1 Aerosols), 72–97.
Spekreijse, H., & Van Der Tweel, L. H. (1965). Linearization of evoked responses to sine wave-modulated light by noise. Nature.
Spekreijse, H., van der Tweel, L. H., & Regan, D. (1972). Interocular sustained suppression: Correlations with evoked potential amplitude and distribution. Vision Research, 12(3), 521–526.
Spekreijse, H., van Norren, D., & van den Berg, T. J. (1971). Flicker responses in monkey lateral geniculate nucleus and human perception. Proceedings of the National Academy of Sciences of the United States of America, 68(11), 2802–2805.
Srinivasan, R., Bibi, F. A., & Nunez, P. L. (2006). Steady-state visual evoked potentials: Distributed local sources and wave-like dynamics are sensitive to flicker frequency. Brain Topography, 18(3), 167–187.
Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology.
Strasburger, H., Scheidler, W., & Rentschler, I. (1988). Amplitude and phase characteristics of the steady-state visual evoked potential. Applied Optics, 27(6), 1069–1088.
Sutoyo, D., & Srinivasan, R. (2009). Nonlinear SSVEP responses are sensitive to the perceptual binding of visual hemifields during conventional “eye” rivalry and interocular “percept” rivalry. Brain Research, 1251, 245–255.
Swami, A., Mendel, C., Nikias, C., 2000. Higher-order spectral analysis (hosa) toolbox. Version 2, 3.
Sweeney-Reed, C. M., & Nasuto, S. J. (2007). A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition. Journal of Computational Neuroscience, 23(1), 79–111.
Thomas Y. Hou, Z. S. (2016). Extracting a shape function for a signal with intra-wave frequency modulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065).
Tobimatsu, S., & Kato, M. (1996). The effect of binocular stimulation on each component of transient and steady-state VEPs. Electroencephalography and Clinical Neurophysiology - Evoked Potentials, 100(3), 177–183.
Tobimatsu, S., Kurita-Tashima, S., Nakayama-Hiromatsu, M., & Kato, M. (1993). Effect of spatial frequency on transient and steady-state VEPs: stimulation with checkerboard, square-wave grating and sinusoidal grating patterns. Journal of the Neurological Sciences, 118(1), 17–24.
Tobimatsu, S., Tashima-Kurita, S., Nakayama-Hiromatsu, M., & Kato, M. (1991). Clinical relevance of phase of steady-state VEPs to P100 latency of transient VEPs. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 80(2), 89–93.
Tort, A. B. L., Komorowski, R., Eichenbaum, H., & Kopell, N. (2010). Measuring Phase-Amplitude Coupling Between Neuronal Oscillations of Different Frequencies. Journal of Neurophysiology, 104(2), 1195–1210.
Trick, G. L., Dawson, W. W., & Compton, J. R. (1982). Interocular luminance differences and the binocular pattern-reversal visual-evoked response. Investigative Ophthalmology and Visual Science, 22(3), 394–401.
Tsai, F. F., Fan, S. Z., Lin, Y. S., Huang, N. E., & Yeh, J. R. (2016). Investigating power density and the degree of nonlinearity in intrinsic components of anesthesia EEG by the hilbert-huang transform: An example using ketamine and alfentanil. PLoS ONE, 11(12), 1–16.
Vaina, L. M., Cowey, A., & Kennedy, D. (1999). Perception of first- and second-order motion: Separable neurological mechanisms? Human Brain Mapping, 7(1), 67–77.
Van Der Tweel, L. H., & Spekreijse, H. (1969). SIGNAL TRANSPORT AND RECTIFICATION IN THE HUMAN EVOKED RESPONSE SYSTEM. Annals of the New York Academy of Sciences, 156(2), 678–695.
van Ede, F., Quinn, A. J., Woolrich, M. W., & Nobre, A. C. (2018). Neural Oscillations: Sustained Rhythms or Transient Burst-Events? Trends in Neurosciences, 41(7), 415–417.
Voloh, B., Valiante, T. A., Everling, S., and Womelsdorf, T. (2015). Theta–
gamma coordination between anterior cingulate and prefrontal cortex indexes
correct attention shifts. Proc. Natl. Acad. Sci. U.S.A. 112, 8457–8462.
Voytek, B., D’Esposito, M., Crone, N., & Knight, R. T. (2013). A method for event-related phase/amplitude coupling. NeuroImage, 64, 416–424.
Vialatte, F. B., Maurice, M., Dauwels, J., & Cichocki, A. (2009). Steady state visual evoked potentials in the delta range (0.5-5 Hz). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5506 LNCS(PART 1), 400–407. https://doi.org/10.1007/978-3-642-02490-0_49
Vialatte, F.-B., Maurice, M., Dauwels, J., & Cichocki, A. (2010). Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in Neurobiology, 90(4), 418–438.
Wanger, P., & Nilsson, B.Y. (1978). Visual evoked responses to pattern-reversal stimulation in patients with amblyopia and/or defective binocular functions. Acta Ophthalmologica, 56, 617–627.
Williams, N., Nasuto, S. J. J., & Saddy, J. D. J. D. (2011). Evaluation of Empirical Mode Decomposition for Event-Related Potential Analysis. EURASIP Journal on Advances in Signal Processing, 2011(1), 965237.
Williams, P. E., & Shapley, R. M. (2007). A Dynamic Nonlinearity and Spatial Phase Specificity in Macaque V1 Neurons. Journal of Neuroscience, 27(21), 5706–5718.
Wu, Z., & Huang, N. E. (2009). Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method. Advances in Adaptive Data Analysis, 01(01), 1–41.
Yeh, C.-H., Lo, M.-T., & Hu, K. (2016). Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals. Physica A: Statistical Mechanics and Its Applications, 454, 143–150.
Zemon, V., Pinkhasov, E., & Gordon, J. (1993). Electrophysiological tests of neural models: evidence for nonlinear binocular interactions in humans. Proceedings of the National Academy of Sciences of the United States, USA, 90, 2975–2978.
Zemon, V., & Ratliff, F. (1984). Intermodulation components of the visual evoked potential: Responses to lateral and superimposed stimuli. Biological Cybernetics, 50(6), 401–408.
Zhang, P., Jamison, K., Engel, S., He, B., & He, S. (2011). Binocular Rivalry Requires Visual Attention. Neuron, 71(2), 362–369.
Zhou, Y. X., & Baker, C. L. (1993). A processing stream in mammalian visual cortex neurons for non-fourier responses. Science, 261(5117), 98–101.

指導教授

阮啟弘(Chi-Hung Juan)

審核日期

2020-5-5

推文