臺灣博碩士論文加值系統

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。

Recent studies have indicated that the phase of brainwaves may reflect neural excitability and thereby affect perceptual and cognitive functions and associated behavioral outcomes. Therefore, further investigation of phase activity may benefit our understanding of the brain mechanism, augment brain functions and facilitate far-reaching clinical applications in the future.
This thesis investigated the causal relationship between visual detection and alpha phase. To explain this relationship, the previous periodic perception theory has posited that visual detection performance could be enhanced at a specific phase but reduced at the opposite phase (by 180 degrees). However, capitalizing on post-hoc correlation and non-invasive brain stimulation approaches, both supporting and opposing evidence has been recently reported . These previous approaches have several methodological limitations and are unable to infer the causal relations between α phase and visual detection, which might potentially lead to inconsistent findings. To address these issues, this thesis aimed to adopt the real-time phase-locked stimulus presentation approach, so as to directly investigate the causal relations between α phase and visual detection.
The phase-locked stimulus presentation approach relies on rapid and accurate real-time phase tracking and prediction to precisely present the visual stimulus at the target phases. To this end, we first developed an adaptive Kalman filtering algorithm. Using synthetic and real signals, our new approach performed better robust relative to the existing Autoregressive model (AR) and Fast Fourier Transform (FFT) algorithms. During the subsequent visual detection experiment, we applied our newly developed approach to real-time analyze Magnetoencephalography (MEG) signals, in order to track, predict α phase so that near-threshold stimuli were presented at the target phase angles (0, 90, 180, 270 degrees). Our results showed that the behavioral performance of visual detection varied according α phase and was especially enhanced at the 90 degree. Although these findings are consistent with the idea of α phase-dependent visual detection, they are slightly different from the periodic perception theory as previously proposed.
With the real-time phase-locked stimulus presentation approach, we provide new insights into the causal relations between α phase and visual detection. Furthermore, our newly developed algorithm could be generally applied in other research domains pertaining to the effect of phase on brain functions .

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

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