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研究生:吳淳羽
研究生(外文):Chun Yu Wu
論文名稱:結合機器學習與腦電訊號之正念減壓訓練成效分類技術
論文名稱(外文):Classification of training effectiveness of mindfulness-based stress reduction using EEG and machine learning approaches
指導教授:趙一平趙一平引用關係
指導教授(外文):Y. P. Chao
學位類別:碩士
校院名稱:長庚大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:99
中文關鍵詞:正念減壓腦電圖功率頻譜密度direct Directed Transfer Function機器學習
外文關鍵詞:Mindfulness-Based Stress Reduction (MBSR)Electroencephalography (EEG)Power Spectral Density (PSD)direct Directed Transfer Function (dDTF)Machine Learning
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  • 點閱點閱:54
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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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