臺灣博碩士論文加值系統

正念減壓訓練已被諸多研究證實能夠緩減不當壓力所造成的負面思考、焦慮等無益於心理健康的行為或情緒，進而改善身心健康與提升生活品質。然而在實際應用情境中，練習成效差的正念訓練者容易因主觀評估而沒有發現自身練習問題，誤以為自身正念訓練表現良好，若能透過腦電神經回饋提供正念訓練成效評估的量化結果，將能協助使用者以客觀角度檢視其正念訓練效果。
腦電圖特徵擷取與演算法是開發腦電神經回饋時所需的兩項關鍵技術，為了建立適用於正念減壓訓練之腦電圖特徵擷取與演算法技術，本研究擷取Beta、Gamma頻帶 (20~40 Hz) 之功率頻譜密度特徵和 Gamma頻帶 (30~40 Hz) 之有效性連結direct Directed Transfer Function 特徵作為訓練資料，搭配邏輯迴歸、支持向量機、多層感知器、貝式分類器、決策樹、邏輯模型樹及隨機森林等七種機器學習演算法，來分類無經驗組 (46 筆)、八周經驗組 (18筆)，以及專家組 (4 筆) 於靜息態、正念呼吸及身體掃描階段之腦電訊號，並藉由 leave-one-out 交叉驗證方法來評估分類結果。
研究結果顯示，不論是使用功率頻譜密度或是dDTF，機器學習演算法在靜息態、正念呼吸及身體掃描三種不同階段下分類無經驗/有八周經驗兩種類別，均有高於 80.0% 準確率之良好表現，且結合有效性連結特徵、主成分分析的特徵擷取、決策樹分類演算法的組合可達到 91.7% 的準確率，代表結合機器學習與功率頻譜密度、有效性連結兩種特徵之技術在評估正念訓練成效上皆具備可實際應用的潛力，除此之外，為了測試此項組合是否適合應用於穿戴式裝置，本研究亦以其為基礎設計了腦電圖電極刪減實驗，由結果可觀察到當電極從 19 個減少至 14 個時，仍能維持原準確率，而當電極最少刪減至 4 個 (F7、F8、T7、P7) 時，亦能保持 80% 以上的準確率，此研究發現為未來發展用於正念減壓訓練成效評估之穿戴式腦電神經回饋提供可參考方法。

Mindfulness-based interventions have been proven to promote physical/mental health and quality of life. However, mindfulness practitioners with insufficient training effectivenesses fail to find the problems easily due to their subjective assessments. EEG-based neurofeedback can be used to solve this problem by providing quantitative assessments of mindfulness training effectiveness, and thus help users evaluate the training effectiveness from objective perspectives.
EEG feature extractions and algorithms are the keys to developing EEG-based neurofeedback. Hence, to establish mindfulness-based neurofeedback, two EEG features, power spectral density (PSD) in beta and gamma band (20 - 40 Hz) and direct directed transfer function (dDTF) in gamma band (30 - 40 Hz), were extracted as training data in this research, along with seven machine learning algorithms (i.e. logistic regression, support vector machine, multilayer perceptron, naïve bayes, decision tree, logistic model tree, and random forest) as classifiers. These features and classifiers were used for classifying the EEG signals of three mindfulness practices (i.e. resting-state, mindfulness breathing, and body scanning) from groups of non-MBSR experience (n = 46), eight-week MBSR experience (n = 18), and MBSR experts (n = 4). In addition, leave-one-out cross-validation was applied for evaluating the classification results.
The results showed that classification methods using EEG feature extraction (i.e. PSD or dDTF), and machine learning had good performances with accuracies above 80% in resting-state, mindfulness breathing, and body-scanning practice. Especially, for the one implementing dDTF, principal component analysis (PCA) feature extraction, and decision tree, with an accuracy of 91.7%. Indicating that the technique combining machine learning with PSD or dDTF has the potential to be applied in evaluating the effectiveness of MBSR training. In addition, to examine whether this combination is suitable for wearable devices, this study also designed an EEG electrode pruning experiment. The experiment results showed that when the number of electrodes was reduced from 19 to 14, the original accuracy could still be maintained at 91.7%, and when the number of electrodes was reduced to 4 (i.e. F7, F8, T7, P7), the accuracy could still be at 80% above. The findings provide a referable method for the future development of wearable devices for the evaluation of MBSR training effectiveness.

目錄
摘要 i
Abstract iii
目錄 v
圖目錄 viii
表目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
1.4 章節說明 5
第二章 文獻探討 6
2.1 正念練習相關腦電訊號頻域變化 6
2.2 使用腦電訊號有效性連結探討大腦網路之研究 12
2.3 機器學習於腦電圖之應用 14
第三章 研究方法 18
3.1 正念減壓資料集 18
3.1.1 實驗參與者 18
3.1.2 實驗流程 19
3.1.3 腦電圖記錄方式及預處理 20
3.1.4 五因素正念量表 21
3.2 研究架構 22
3.3 腦電訊號處理 23
3.3.1 功率頻譜密度 23
3.3.2 有效性連結 dDTF 25
3.4 機器學習演算法 27
3.4.1 資料集分類、平衡與離群值排除 27
3.4.2 使用之演算法及訓練環境 30
3.4.3 模型訓練及交叉驗證 32
3.5 實驗設計 34
3.5.1 實驗(一) 無經驗組與有經驗組於功率頻譜密度之二元分類 35
3.5.2 實驗(二) 無經驗組與八周經驗組於功率頻譜密度之二元分類 36
3.5.3 實驗(三) 無經驗組與有經驗組於有效性連結指標之二元分類 37
3.5.4 實驗(四) 無經驗組與八周經驗組於有效性連結指標之二元分類 38
第四章 結果與討論 39
4.1 實驗(一) 無經驗/有經驗組於功率頻譜密度之二元分類 39
4.2 實驗(二) 無經驗/八周經驗組於功率頻譜密度之二元分類 40
4.3 實驗(三) 無經驗/有經驗組於 dDTF 之二元分類 43
4.4 實驗(四) 無經驗/八周經驗組於 dDTF 之二元分類 45
4.5 不同腦電訊號特徵於各正念訓練階段之分類準確率比較 47
4.6 各筆資料分類結果分析 49
4.7 決策樹對靜息態 dDTF 資料分類結果分析 51
4.8 基於 DT-dDTF 結果之電極刪減實驗 59
4.9 電極選取差異對於腦電訊號分類準確率之影響 59
4.10 即時腦電神經回饋 63
4.11 研究限制 63
第五章 結論與未來展望 64
參考文獻 66
附錄一、DT-dDTF 相關電極及其所對應之大腦皮質區域與大腦網路 79
附錄二、基於決策樹之 dDTF 電極刪減實驗 80


圖目錄
圖 3.1 1、EEG 與 fMRI 訊號同步量測實驗示意圖 20
圖 3.2 1、系統架構圖 23
圖 3.4 1、正念減壓資料集重新分組方式 28
圖 3.4 2、資料集平衡實作方式示意圖 30
圖 3.4 3、混淆矩陣示意圖 33
圖 4.5 1、不同特徵於各階段之無/有經驗分類準確率 48
圖 4.5 2、不同特徵於各階段之無/八周經驗分類準確率 48
圖 4.6 1、控制組與正念減壓組的五因素正念量表總分變化 50
圖 4.7 1、決策樹使用之靜息態特徵整理範例 51
圖 4.7 2、各 dDTF 屬性被決策樹使用次數 52
圖 4.7 3、DT-dDTF 特徵於大腦皮質位置示意圖 53


表目錄
表 2.1 1、過往研究發現之正念練習相關頻率變化 (由低至高頻率排序) 7
表 2.3 1、使用機器學習演算法進行腦電訊號頻域或有效性連結特徵分類之過往研究 16
表 3.5 1、本研究使用變數組合及其所對應實驗 34
表 3.5 2、實驗(一)相關變數 35
表 3.5 3、實驗(二)相關變數 36
表 3.5 4、實驗(三)相關變數 37
表 3.5 5、實驗(四)相關變數 38
表 4.1 1、不同模型對實驗一靜息態功率頻譜密度之分類結果 39
表 4.1 2、不同模型對實驗一正念呼吸功率頻譜密度之分類結果 40
表 4.1 3、不同模型對實驗一身體掃描功率頻譜密度之分類結果 40
表 4.2 1、不同模型對實驗二靜息態功率頻譜密度之分類結果 41
表 4.2 2、不同模型對實驗二正念呼吸功率頻譜密度之分類結果 42
表 4.2 3、不同模型對實驗二身體掃描功率頻譜密度之分類結果 42
表 4.3 1、不同模型對實驗三靜息態dDTF之分類結果 43
表 4.3 2、不同模型對實驗三正念呼吸dDTF之分類結果 44
表 4.3 3、不同模型對實驗三身體掃描dDTF之分類結果 44
表 4.4 1、不同模型對實驗四靜息態dDTF之分類結果 46
表 4.4 2、不同模型對實驗四正念呼吸dDTF之分類結果 46
表 4.4 3、不同模型對實驗四身體掃描dDTF之分類結果 47
表 4.7 1、決策樹分類所使用之 dDTF 特徵 53
表 4.7 2、正念相關大腦皮質活動過往研究與本研究發現對照表 55
表 4.7 3、正念相關大腦網路活動過往研究與本研究發現對照表 58
表 4.9 1、用於功率頻譜密度特徵之不同電極選擇方式 60
表 4.9 2、不同電極組合於靜息態功率頻譜密度之決策樹分類結果 61
表 4.9 3、不同演算法於Exp 4.9.1-1特徵之分類結果 62

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