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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101506
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dc.contributor.advisor宋孔彬zh_TW
dc.contributor.advisorKung-Bin Sungen
dc.contributor.author莊棨翔zh_TW
dc.contributor.authorChi-Hsiang Chuangen
dc.date.accessioned2026-02-04T16:19:57Z-
dc.date.available2026-02-05-
dc.date.copyright2026-02-04-
dc.date.issued2026-
dc.date.submitted2026-01-29-
dc.identifier.citation[1] N. K. Logothetis, “What we can do and what we cannot do with fMRI,” Nature, vol. 453, no. 7197, pp. 869–878, Jun. 2008, doi: 10.1038/nature06976.
[2] P. Pinti et al., “Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real world Cognitive Tasks,” J. Vis. Exp. JoVE, no. 106, p. e53336, Dec. 2015, doi: 10.3791/53336.
[3] P. Pinti, L. M. Dinu, and T. J. Smith, “Ecological functional near-infrared spectroscopy in mobile children: using short separation channels to correct for systemic contamination during naturalistic neuroimaging,” Neurophotonics, vol. 11, no. 4, p. 045004, Oct. 2024, doi: 10.1117/1.NPh.11.4.045004.
[4] H.-J. Hwang et al., “Toward more intuitive brain–computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy,” J. Biomed. Opt., vol. 21, no. 9, p. 091303, Apr. 2016, doi: 10.1117/1.JBO.21.9.091303.
[5] F. Herold, P. Wiegel, F. Scholkmann, and N. G. Müller, “Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise Cognition Science: A Systematic, Methodology-Focused Review,” J. Clin. Med., vol. 7, no. 12, p. 466, Dec. 2018, doi: 10.3390/jcm7120466.
[6] S. Brigadoi and R. J. Cooper, “How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics, vol. 2, no. 2, p. 025005, May 2015, doi: 10.1117/1.NPh.2.2.025005.
[7] S. Brigadoi and R. J. Cooper, “How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy,” NEUROPHOTONICS, vol. 2, no. 2, p. 025005, Jun. 2015, doi: 10.1117/1.NPh.2.2.025005.
[8] L. Kocsis, P. Herman, and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol., vol. 51, no. 5, p. N91, Feb. 2006, doi: 10.1088/0031 9155/51/5/N02.
[9] I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics, vol. 3, no. 3, p. 031405, Mar. 2016, doi: 10.1117/1.NPh.3.3.031405.
[10] E. Kirilina et al., “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” NeuroImage, vol. 61, no. 1, pp. 70–81, May 2012, doi: 10.1016/j.neuroimage.2012.02.074.
[11] T. Sato et al., “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” NeuroImage, vol. 141, pp. 120–132, Nov. 2016, doi: 10.1016/j.neuroimage.2016.06.054.
[12] L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, and D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” NeuroImage, vol. 59, no. 3, pp. 2518–2528, Feb. 2012, doi: 10.1016/j.neuroimage.2011.08.095.
[13] Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work?,” NeuroImage, vol. 45, no. 3, pp. 788–794, Apr. 2009, doi: 10.1016/j.neuroimage.2008.12.048.
[14] “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging.” Accessed: Apr. 24, 2025. [Online]. Available: https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume 10/issue-1/011014/Eigenvector-based-spatial-filtering-for-reduction-of physiological-interference-in/10.1117/1.1852552.full
[15] S. Kohno et al., “Removal of the skin blood flow artifact in functional near infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt., vol. 12, no. 6, p. 062111, Nov. 2007, doi: 10.1117/1.2814249.
[16] D. Wyser, M. Mattille, M. Wolf, O. Lambercy, F. Scholkmann, and R. Gassert, “Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics,” NEUROPHOTONICS, vol. 7, no. 3, p. 035011, Sep. 2020, doi: 10.1117/1.NPh.7.3.035011.
[17] F. Zhang, D. Cheong, A. F. Khan, Y. Chen, L. Ding, and H. Yuan, “Correcting physiological noise in whole-head functional near-infrared spectroscopy,” J. Neurosci. Methods, vol. 360, p. 109262, Aug. 2021, doi: 10.1016/j.jneumeth.2021.109262.
[18] L. Gagnon, M. A. Yücel, D. A. Boas, and R. J. Cooper, “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” NeuroImage, vol. 85, pp. 127–135, Jan. 2014, doi: 10.1016/j.neuroimage.2013.01.073.
[19] I. de Roever, G. Bale, R. J. Cooper, and I. Tachtsidis, “Functional NIRS Measurement of Cytochrome-C-Oxidase Demonstrates a More Brain-Specific Marker of Frontal Lobe Activation Compared to the Haemoglobins,” in Oxygen Transport to Tissue XXXIX, H. J. Halpern, J. C. LaManna, D. K. Harrison, and B. Epel, Eds., Cham: Springer International Publishing, 2017, pp. 141–147. doi: 10.1007/978-3-319-55231-6_19.
[20] S. Brigadoi et al., “Image reconstruction of oxidized cerebral cytochrome C oxidase changes from broadband near-infrared spectroscopy data,” Neurophotonics, vol. 4, no. 2, p. 021105, May 2017, doi: 10.1117/1.NPh.4.2.021105.
[21] C. Kolyva et al., “Systematic investigation of changes in oxidized cerebral cytochrome c oxidase concentration during frontal lobe activation in healthy adults,” Biomed. Opt. Express, vol. 3, no. 10, pp. 2550–2566, Oct. 2012, doi: 10.1364/BOE.3.002550.
[22] G. Bale, S. Mitra, J. Meek, N. Robertson, and I. Tachtsidis, “A new broadband near-infrared spectroscopy system for in-vivo measurements of cerebral cytochrome-c-oxidase changes in neonatal brain injury,” Biomed. Opt. Express, vol. 5, no. 10, pp. 3450–3466, Oct. 2014, doi: 10.1364/BOE.5.003450.
[23] P. R. Rich, A. J. Moody, and W. J. Ingledew,“Detection of a near infra-red absorption band of ferrohaem a₃ in cytochrome c oxidase,”FEBS Letters, vol. 305, no. 3, pp. 171–173, 1992, doi: 10.1016/0014-5793(92)80659-5.
[24] 伍育汶, “功能性近紅外光譜術應用於經顱紅外光刺激前後之認知功能評估,” 碩士論文, 國立臺灣大學生醫電子與資訊學研究所, 台北市, 2022.
[25] 潘韋翰, “功能性近紅外光譜術測量經顱紅外光刺激以及延遲匹配樣本任務之 吸收變化,” 碩士論文, 國立臺灣大學生醫電子與資訊學研究所, 台北市, 2021. [Online]. Available: https://hdl.handle.net/11296/pgxcwz
[26] M. A. Yücel, J. Selb, D. A. Boas, S. S. Cash, and R. J. Cooper, “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” NeuroImage, vol. 85, pp. 192–201, Jan. 2014, doi: 10.1016/j.neuroimage.2013.06.054.
[27] S. M. Jaeggi, M. Buschkuehl, W. J. Perrig, and B. Meier, “The concurrent validity of the N-back task as a working memory measure,” Memory, vol. 18, no. 4, pp. 394–412, May 2010, doi: 10.1080/09658211003702171.
[28] L. S. van Velzen, C. Vriend, S. J. de Wit, and O. A. van den Heuvel, “Response Inhibition and Interference Control in Obsessive–Compulsive Spectrum Disorders,” Front. Hum. Neurosci., vol. 8, p. 419, Jun. 2014, doi: 10.3389/fnhum.2014.00419.
[29] Y. Song and Y. Hakoda, “An Asymmetric Stroop/Reverse-Stroop Interference Phenomenon in ADHD,” J. Atten. Disord., vol. 15, no. 6, pp. 499–505, Aug. 2011, doi: 10.1177/1087054710367607.
[30] L. M. Jacola et al., “Clinical utility of the N-back task in functional neuroimaging studies of working memory,” J. Clin. Exp. Neuropsychol., vol. 36, no. 8, pp. 875 886, Sep. 2014, doi: 10.1080/13803395.2014.953039.
[31] A. Meule, “Reporting and Interpreting Working Memory Performance in n-back Tasks,” Front. Psychol., vol. 8, p. 352, Mar. 2017, doi: 10.3389/fpsyg.2017.00352.
[32] S. Jahani, S. K. Setarehdan, D. A. Boas, and M. A. Yücel, “Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering,” Neurophotonics, vol. 5, no. 1, p. 015003, Feb. 2018, doi: 10.1117/1.NPh.5.1.015003.
[33] S. Brigadoi et al., “Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data,” NeuroImage, vol. 85, pp. 181–191, Jan. 2014, doi: 10.1016/j.neuroimage.2013.04.082.
[34] T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt., vol. 48, no. 10, pp. D280–D298, Apr. 2009, doi: 10.1364/ao.48.00d280.
[35] M. Hiraoka et al., “A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy,” Phys. Med. Biol., vol. 38, no. 12, p. 1859, Feb. 1993, doi: 10.1088/0031-9155/38/12/011.
[36] F. Klein, M. Lührs, A. Benitez-Andonegui, P. Roehn, and C. Kranczioch, “Performance comparison of systemic activity correction in functional near infrared spectroscopy for methods with and without short distance channels,” Neurophotonics, vol. 10, no. 1, p. 013503, Oct. 2022, doi: 10.1117/1.NPh.10.1.013503.
[37] X. Zhou, G. Sobczak, C. M. McKay, and R. Y. Litovsky, “Comparing fNIRS signal qualities between approaches with and without short channels,” PLOS ONE, vol. 15, no. 12, p. e0244186, Dec. 2020, doi: 10.1371/journal.pone.0244186.
[38] T.-C. Kao and K.-B. Sung, “Quantifying tissue optical properties of human heads in vivo using continuous-wave near-infrared spectroscopy and subject-specific three-dimensional Monte Carlo models,” J. Biomed. Opt., vol. 27, no. 8, p. 083021, Jun. 2022, doi: 10.1117/1.JBO.27.8.083021.
[39] H. Sato, M. Kiguchi, F. Kawaguchi, and A. Maki, “Practicality of wavelength selection to improve signal-to-noise ratio in near-infrared spectroscopy,” NeuroImage, vol. 21, no. 4, pp. 1554–1562, Apr. 2004, doi: 10.1016/j.neuroimage.2003.12.017.
[40] H.-D. Nguyen, S.-H. Yoo, M. R. Bhutta, and K.-S. Hong, “Adaptive filtering of physiological noises in fNIRS data,” Biomed. Eng. OnLine, vol. 17, no. 1, p. 180, Dec. 2018, doi: 10.1186/s12938-018-0613-2.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101506-
dc.description.abstract功能性近紅外光光譜(Functional Near Infrared Spectroscopy, fNIRS)技術透過非侵入式光學方式在頭皮表面接收組織漫散射的漫反射光光譜,並透過修正比爾-朗伯定律(MBLL)定量大腦活動時的血液動力學反應。使用寬頻的光源除了可以定量含氧血紅素(Oxygenated Hemoglobin, HbO)、缺氧血紅素(Deoxygenated Hemoglobin, HHb)的濃度變化量,也可以定量與大腦能量代謝直接相關的氧化態細胞色素c氧化酶(oxCCO)的濃度變化。
然而,fNIRS依靠漫反射光的原理使其易受淺層干擾,且淺層干擾也不容易透過傳統的訊號處理流程去除。因此研究者使用短通道獨立量測行經淺層組織的漫反射光譜,並運用不同的方法處理短通道資訊。然而,淺層組織的血液動力學反應可能具有空間異質性,因此傳統上僅使用靠近長通道光源的短通道方法可能有其侷限性。
本研究使用寬頻的鹵素光源,並在長通道的光源與偵測端各設置一個短通道,對五位受試者進行延遲匹配樣本任務(Delayed Matching to Sample, DMS)、Stroop 色彩-詞彙測驗(Stroop Color and Word Test, Stroop)、n-back 任務等認知測驗,以定量出大腦在活化時三種吸收物質的濃度變化,並計算HbO, HHb及oxCCO濃度變化的訊號品質指標。在兩種及三種吸收物質的假設下,驗證殘差光譜(Residual Difference)形狀是否與 oxCCO 的莫爾消光係數(Molar Extinction Coefficient)光譜相似並具跨通道的一致性,藉此驗證三種吸收物質的假設能有效提取oxCCO的濃度變化。除了使用不分層的MBLL模型定量出的血液動力學變化之外,也使用雙層MBLL 的模型中搭配長通道光源附近的短通道偵測器或是長通道偵測器旁的短通道光源,驗證使用短通道能提升擬合成效及訊號品質的假設。並比較使用兩種不同的短通道以及同時使用雙短通道進行MBLL在擬合成效及訊號品質的變化是否與淺層組織血液動力學的異質性相關。並透過長短通道血液動力學之間的相關性試圖找出最佳的短通道選擇。
實驗結果顯示殘差光譜與oxCCO吸收光譜形狀相似且殘差光譜形狀具有跨通道的一致性。使用短通道搭配雙層組織模型相較於不使用短通道的單層組織模型的訊號對比度較高。在雙短通道的淺層組織血液動力學異質性較高時,選用其中一種短通道的訊號品質較佳。而雙短通道的淺層組織血液動力學異質性較低時,三種短通道方法的訊號品質差異不大。最後,基準光譜期間長短通道的血液動力學相關性對於訊號品質並無明顯的影響。
zh_TW
dc.description.abstractFunctional near-infrared spectroscopy (fNIRS) is a non-invasive optical technique that measures diffusely reflected spectra from biological tissues at the scalp surface to quantify cerebral hemodynamic responses associated with brain activity using the Modified Beer–Lambert Law (MBLL). The use of broadband light sources enables not only the quantification of concentration changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HHb), but also the assessment of concentration changes in oxidized cytochrome-c-oxidase (oxCCO), which is directly related to cerebral oxygen metabolism.
However, due to its reliance on diffuse reflectance, fNIRS measurements are highly susceptible to contamination from superficial tissues, and such interference is difficult to remove using conventional signal processing techniques. To address this issue, shortseparation channels are commonly used to independently measure diffuse reflectance originating from superficial tissues, and various strategies have been proposed to incorporate short-channel information. However, superficial hemodynamic responses may exhibit spatial heterogeneity, suggesting that conventional approaches relying on a single short channel located near the source may have inherent limitations.
In this study, a broadband light source was employed, and two short-separation channels were implemented, with one placed near the source and the other near the detector of the long channel. Five healthy participants performed a series of cognitive tasks, including the Delayed Matching-to-Sample, Stroop Color and Word Test, and nback tasks. Concentration changes of HbO, HHb, and oxCCO during brain activation were quantified, and signal quality metrics for each chromophore were evaluated. Under both two- and three-chromophore assumptions, the similarity between the residual difference spectra and the molar extinction coefficient spectrum of oxCCO, as well as their consistency across channels, were examined to validate the effectiveness of the three-chromophore model in extracting oxCCO concentration changes.
In addition to hemodynamic quantification using a conventional single-layer MBLL model, a two-layer MBLL model was applied with short-separation channels. These configurations were used to evaluate the hypothesis that incorporating short-separation channels improves MBLL fitting performance and signal quality. Furthermore, the effects of using two distinct short-separation channels individually or jointly on fitting performance and signal quality were compared, and their relationships with the heterogeneity of superficial tissue hemodynamics were investigated. Finally, correlations between hemodynamic signals measured from long and short channels were analyzed to identify optimal short-channel selection strategies.
The experimental results demonstrate that the residual difference spectra are similar in shape to the molar extinction coefficient spectrum of oxCCO, and that the shape of the residual difference spectra shows consistency across channels. Compared with a single-layer head model without short-channel correction, the use of short channels in combination with a two-layer head model yields higher contrast. When superficial tissue hemodynamic heterogeneity between the two short channels is high, selecting one of the short channels results in better signal quality. In contrast, when superficial tissue hemodynamic heterogeneity between the two short channels is low, the differences in signal quality among the three short-channel approaches are minimal. Finally, the hemodynamic correlation between long- and short-separation channels during the baseline period does not have a significant impact on signal quality.
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dc.description.tableofcontents口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract v
目次 vii
圖次 ix
表次 xi
第1章 研究背景與目標 1
1.1 功能性近紅外光譜原理 1
1.1.1 大腦神經血管耦合與血液動力學變化 1
1.1.2 非侵入性近紅外光譜測量原理與硬體架構 3
1.1.3 修正比爾-朗伯定律 5
1.2 FNIRS量測定量大腦活動與血液動力學常見的方法 8
1.3 研究目標 12
1.3.1 從兩種與三種吸收物質假設下的殘差光譜驗證系統可量測oxCCO的變化 12
1.3.2 使用短通道進行雙層組織模型MBLL的效益 12
1.3.3 淺層組織血液動力學異質性對三種短通道組合訊號品質的影響 13
1.3.4 由長短通道基準光譜血液動力學相關性選擇適合的短通道 13
1.4 研究架構 14
第2章 研究方法 14
2.1 實驗硬體系統 14
2.1.1 實驗光路 14
2.1.2 光纖探頭設計、固定方式及擺放位置 20
2.1.3 光源切換快門的運作方式 25
2.2 大腦認知功能測驗 29
2.2.1 延遲匹配樣本任務 29
2.2.2 Stroop任務 31
2.2.3 n-Back任務 32
2.3 實驗流程 33
2.4 資料分析流程 34
2.4.1 光譜維度前處理 34
2.4.2 時間維度前處理 35
2.4.3 人體晃動造成訊號失真 37
2.4.4 單層組織模型修正比爾-朗伯定律 39
2.4.5 雙層組織模型修正比爾-朗伯定律 39
2.4.6 光子平均路徑長 43
2.4.7 計算擬合成效 44
2.4.8 吸收物質濃度變化的訊號品質指標 45
第3章 實驗結果 49
3.1 活化通道篩選 49
3.2 比較兩種與三種吸收物質假設下的擬合成效 51
3.3 使用短通道對訊號品質的影響 54
3.4 淺層組織血液動力學異質性對三種短通道組合訊號品質的影響 58
3.5 由長短通道基準光譜血液動力學相關性選擇適合的短通道 62
第4章 研究限制與未來展望 67
4.1 硬體架構 67
4.2 分析方法 68
4.2.1 定量oxCCO的證據 68
4.2.2 跨短通道方法的比較 69
4.2.3 光子平均路徑長 69
4.3 實驗設計 70
Reference 71
附錄 75
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dc.language.isozh_TW-
dc.subject功能性近紅外光光譜-
dc.subject短通道量測-
dc.subject修正比爾-朗伯定律-
dc.subject氧化態細胞色素c氧化酶-
dc.subject大腦認知功能測驗-
dc.subjectFunctional near-infrared spectroscopy-
dc.subjectshort-separation measurements-
dc.subjectModified Beer-Lambert Law-
dc.subjectoxidized cytochrome-c-oxidase-
dc.subjectcognitive tasks-
dc.title以寬頻雙短通道功能性近紅外光譜系統分析訊號品質與組織血液動力學之關聯性zh_TW
dc.titleAnalyzing the Relations of Signal Quality and Tissue Hemodynamics with a Double Short-Channel Broadband Functional Near-Infrared Spectroscopy Systemen
dc.typeThesis-
dc.date.schoolyear114-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee盧家鋒;劉宴齊zh_TW
dc.contributor.oralexamcommitteeChia-Feng Lu ;Yan-Ci Liuen
dc.subject.keyword功能性近紅外光光譜,短通道量測修正比爾-朗伯定律氧化態細胞色素c氧化酶大腦認知功能測驗zh_TW
dc.subject.keywordFunctional near-infrared spectroscopy,short-separation measurementsModified Beer-Lambert Lawoxidized cytochrome-c-oxidasecognitive tasksen
dc.relation.page75-
dc.identifier.doi10.6342/NTU202600210-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2026-01-30-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept生醫電子與資訊學研究所-
dc.date.embargo-lift2026-02-05-
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