請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79416完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳志宏(Jyh-Horng Chen) | |
| dc.contributor.author | Chih-Hsin Tseng | en |
| dc.contributor.author | 曾至新 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:00:00Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-23T09:00:00Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | Pfurtscheller, G., A. Stancak Jr, and C. Neuper, Event-related synchronization (ERS) in the alpha band—an electrophysiological correlate of cortical idling: a review. International journal of psychophysiology, 1996. 24(1-2): p. 39-46. Jensen, O., et al., Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing. Trends Neurosci, 2014. 37(7): p. 357-69. Klimesch, W., alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci, 2012. 16(12): p. 606-17. Sadaghiani, S. and A. Kleinschmidt, Brain Networks and alpha-Oscillations: Structural and Functional Foundations of Cognitive Control. Trends Cogn Sci, 2016. 20(11): p. 805-817. Hughes, S.W. and V. Crunelli, Thalamic mechanisms of EEG alpha rhythms and their pathological implications. The Neuroscientist, 2005. 11(4): p. 357-372. Dosenbach, N.U., et al., Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A, 2007. 104(26): p. 11073-8. Seeley, W.W., et al., Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 2007. 27(9): p. 2349-2356. Bompas, A., et al., The contribution of pre-stimulus neural oscillatory activity to spontaneous response time variability. Neuroimage, 2015. 107: p. 34-45. Ruzzoli, M., et al., The relevance of alpha phase in human perception. Cortex, 2019. 120: p. 249-268. Benwell, C.S.Y., et al., Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance. eNeuro, 2017. 4(6). Sauseng, P. and W. Klimesch, What does phase information of oscillatory brain activity tell us about cognitive processes? Neurosci Biobehav Rev, 2008. 32(5): p. 1001-13. VanRullen, R., Perceptual Cycles. Trends Cogn Sci, 2016. 20(10): p. 723-735. VanRullen, R. and C. Koch, Is perception discrete or continuous? Trends in Cognitive Sciences, 2003. 7(5): p. 207-213. Kasten, F.H. and C.S. Herrmann, Discrete sampling in perception via neuronal oscillations-Evidence from rhythmic, non-invasive brain stimulation. Eur J Neurosci, 2020. Valera, F.J., et al., Perceptual framing and cortical alpha rhythm. Neuropsychologia, 1981. 19(5): p. 675-686. Busch, N.A., J. Dubois, and R. VanRullen, The phase of ongoing EEG oscillations predicts visual perception. J Neurosci, 2009. 29(24): p. 7869-76. Helfrich, R.F., et al., Entrainment of brain oscillations by transcranial alternating current stimulation. Curr Biol, 2014. 24(3): p. 333-9. Jaegle, A. and T. Ro, Direct control of visual perception with phase-specific modulation of posterior parietal cortex. J Cogn Neurosci, 2014. 26(2): p. 422-32. Mathewson, K.E., et al., To see or not to see: prestimulus alpha phase predicts visual awareness. J Neurosci, 2009. 29(9): p. 2725-32. de Graaf, T.A., et al., Does alpha phase modulate visual target detection? Three experiments with tACS-phase-based stimulus presentation. Eur J Neurosci, 2020. 51(11): p. 2299-2313. Vigué‐Guix, I., et al., Can the occipital alpha‐phase speed up visual detection through a real‐time EEG‐based brain–computer interface (BCI)? European Journal of Neuroscience, 2020. Lakatos, P., J. Gross, and G. Thut, A New Unifying Account of the Roles of Neuronal Entrainment. Curr Biol, 2019. 29(18): p. R890-R905. Bruers, S. and R. VanRullen, At What Latency Does the Phase of Brain Oscillations Influence Perception? eNeuro, 2017. 4(3). Zrenner, C., et al., Closed-Loop Neuroscience and Non-Invasive Brain Stimulation: A Tale of Two Loops. Front Cell Neurosci, 2016. 10: p. 92. Bergmann, T.O., Brain State-Dependent Brain Stimulation. Front Psychol, 2018. 9: p. 2108. Shirinpour, S., et al., Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation. Journal of neural engineering, 2020. 17(4): p. 046002. Chen, L.L., et al., Real-time brain oscillation detection and phase-locked stimulation using autoregressive spectral estimation and time-series forward prediction. IEEE Trans Biomed Eng, 2013. 60(3): p. 753-62. Blackwood, E., M.-c. Lo, and A.S. Widge. Continuous phase estimation for phase-locked neural stimulation using an autoregressive model for signal prediction. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2018. IEEE. Zrenner, C., et al., The shaky ground truth of real-time phase estimation. Neuroimage, 2020. 214: p. 116761. Mansouri, F., et al., A Fast EEG Forecasting Algorithm for Phase-Locked Transcranial Electrical Stimulation of the Human Brain. Front Neurosci, 2017. 11: p. 401. Rodriguez Rivero, C. and J. Ditterich, A user-friendly algorithm for adaptive closed-loop phase-locked stimulation. J Neurosci Methods, 2021. 347: p. 108965. Simon, D., Optimal state estimation: Kalman, H infinity, and nonlinear approaches. 2006: John Wiley Sons. Sudre, G., et al., rtMEG: a real-time software interface for magnetoencephalography. Computational intelligence and neuroscience, 2011. 2011: p. 11. Oostenveld, R., et al., FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational intelligence and neuroscience, 2011. 2011. Kleiner, M., D. Brainard, and D. Pelli, What's new in Psychtoolbox-3? 2007. Green, D.M. and J.A. Swets, Signal detection theory and psychophysics. Vol. 1. 1966: Wiley New York. Chou, E.P. and S.-M. Hsu, Cosine similarity as a sample size-free measure to quantify phase clustering within a single neurophysiological signal. Journal of neuroscience methods, 2018. 295: p. 111-120. Miller, J., T. Patterson, and R. Ulrich, Jackknife-based method for measuring LRP onset latency differences. Psychophysiology, 1998. 35(1): p. 99-115. Taylor, J.R., et al., The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage, 2017. 144(Pt B): p. 262-269. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79416 | - |
| dc.description.abstract | "近來的許多研究顯示腦波相位可能反映大腦的活性狀態,許多視覺、認知功能和行為反應可能受腦波相位所調控。因此腦波相位的探究除了有助於進一步了解大腦運作的機制,未來藉由外加或內生性操控來左右腦波的相位,將可能有助於增益大腦的功能甚至嘉惠生醫臨床上的應用。 本論文著重於視覺偵測與腦波相位的議題。視覺偵測多被認為與α腦波相位有所關聯,為了解釋此關聯性,譬如週期視覺(periodic perception)理論認為:視覺偵測能力會在特定相位點增強,卻在與之180度相反的相位點降低。但是近期的研究卻對於此觀點提出諸多正反不同的論證。這些研究大多採用事後相關(post-hoc correlation)分析或是非侵入性大腦刺激(non-invasive brain stimulation) 的研究方式。但這些方法有許多方法學上的限制,此外也無法精準地了解兩者的因果關係,這種種導致之前文獻無法得到一致性的結論。綜上所述,此議題目前仍未有定論,因此本研究將採用即時相位鎖定(phase-locked)的方式,改正上述研究方法的缺失以利能更直接的探討α腦波相位與視覺偵測的因果關係。 由於本研究仰賴精確快速地即時大腦相位偵測,以便視覺刺激物能準確地出現在指定的α腦波相位上,也就是所謂的相位鎖定刺激物呈現(phase-locked stimulus presentation)。為此我們首先發展了新的適應性卡爾曼濾波(Adaptive Kalman)相位偵測演算法。結果發現無論利用合成或真實腦波訊號,此新方法普遍優於過往奠基在自迴歸模型(Autoregressive model, AR)和快速傅立葉轉換(Fast Fourier Transform, FFT)的演算法。在下階段視覺偵測的實驗過程中,我們應用新發展的演算法即時分析腦磁圖(Magnetoencephalography, MEG)記錄之腦波,藉此追蹤、預測α腦波相位和呈現鎖定在特定α相位的刺激物。實驗結果顯示視覺偵測表現確實會隨著α腦波相位(0,90,180,270度)變化,特別在90度時偵測能力會顯著提升。此結果雖支持α腦波相位能影響視覺偵測,但卻略異於前人所提出的週期視覺理論。 藉由即時相位鎖定的研究方式,本研究對於α腦波相位與視覺偵測的因果關係提出了新的論證。此外,新發產出的相位偵測演算法也將可應用於探索與腦波相位相關的其他大腦功能的研究中。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:00:00Z (GMT). No. of bitstreams: 1 U0001-1910202110593200.pdf: 5024943 bytes, checksum: 6b202d3fdb31ef77df2847da05bacf28 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii 英文摘要 v 目錄 vii 圖目錄 x 表目錄 xii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目標 2 1.4 論文架構 2 第二章 腦波相位與視覺偵測 4 2.1 α波的生理機制 4 2.2 相位編碼理論 5 2.3 α腦波相位與視覺偵測 6 2.3.1 事後相關分析法 7 2.3.2 非侵入性大腦刺激法 9 2.3.3 即時相位鎖定刺激物呈現法 11 2.4 自迴歸模型法與快速傅立葉法 12 2.5 發展適應性卡爾曼方法 14 第三章 方法與模擬結果 16 3.1 流程圖 16 3.2 確立離線處理 19 3.3 即時資料取入 19 3.4 前處理 20 3.5 計算訊號最新週期 20 3.6 預測目標相位與呈現實驗刺激 21 3.6.1 自迴歸模型方法 22 3.6.2 快速傅立葉轉換方法 22 3.6.3 適應性卡爾曼濾波方法 22 3.7 訊號測試與比較 25 3.7.1 合成訊號 25 3.7.2 真實訊號 30 3.8 AKF與AR、FFT的比較 38 第四章 系統線上測試 41 4.1 環境與設備 41 4.2 MEG緩衝區資料更新延遲 42 4.3 觸發訊號延遲 45 4.4 適應性卡爾曼計算時間 47 4.5 視覺刺激物呈現延遲 48 4.6 即時預測 50 第五章 實驗與分析 53 5.1 實驗設計 53 5.2 資料前處理 55 5.3 分析方式1:目標相位分類 55 5.4 餘弦相似性與phase reset 58 5.5 分析方式2:離線預測相位分類 60 第六章 討論、結論與未來工作 64 6.1 討論 64 6.1.1 適應性卡爾曼法之參數設計 64 6.1.2 α相位與視覺偵測表現結果 66 6.1.3 適應性卡爾曼演算法的限制 67 6.2 結論 69 6.3 未來工作 70 參考文獻 73 | |
| dc.language.iso | zh-TW | |
| dc.title | α腦波相位與視覺偵測之因果關聯:即時大腦相位鎖定法的探究 | zh_TW |
| dc.title | The causal inference of α phase on visual detection: a real-time phase-locked stimulus presentation approach | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 徐慎謀(Shen-Mou Hsu) | |
| dc.contributor.oralexamcommittee | 王鈺強(Hsin-Tsai Liu),黃從仁(Chih-Yang Tseng),吳昌衛,廖書賢 | |
| dc.subject.keyword | α腦波相位,視覺偵測,相位鎖定刺激物呈現,適應性卡爾曼濾波,腦磁圖, | zh_TW |
| dc.subject.keyword | α phase,visual detection,phase-locked stimulus presentation,adaptive Kalman,Magnetoencephalography, | en |
| dc.relation.page | 75 | |
| dc.identifier.doi | 10.6342/NTU202103861 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位: | 生醫電子與資訊學研究所 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1910202110593200.pdf | 4.91 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
