基於導數尖波及型態學方法提取有效想像運動腦波

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姓名

黃于洲(Yu-Chou Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於導數尖波及型態學方法提取有效想像運動腦波
(Extract effective imaginary motion brain waves based on derivative spike and morphology method)

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至系統瀏覽論文 (2026-8-16以後開放)

摘要(中)

本論文基於導數有效想像運動法及型態學法來針對原始想像運動做有效區間判斷，並利用動態時間規畫法來進行有效想像運動時間點的驗證。會利用腦波試驗段中樣本數的特性做有效想像運動開始點的判斷，根據訊號的形狀，峰值，維持時間，通道間訊號的關聯性來進行有效想像運動演算法的計算。
將原始腦波訊號做完資料擷取的部分後，便送入三種機器學習分類器中分別做想像運動辨識率的判斷，目的是嘗試觀察是否會因不同分類器而造成不同效果回饋。最後判斷的分類效果才能成為有效命令給予腦機介面做判斷。
資料集的部分在公開資料集中提升辨識效率達到5.61%，而在非公開資料集的部分提升辨識效率達到5.93%，因此不僅可以有效降低資料樣本數的個數，亦可以使最後判定辨識率達到優化的效果。

摘要(英)

The thesis is based on the derivative effective imaginary movement method and morphology method to judge the effective interval of the original imaginary movement. Moreover, the thesis uses the dynamic time warping method to verify the effective imaginary movement time point. The characteristics of the number of samples in the brain wave test segment will be used to determine the starting point of the effective imaginary movement. Then, the effective imaginary movement algorithm will be calculated according to the shape of the signal, the peak value, the maintenance time, and the correlation of the signal between channels.
Once the original signal (Electroencephalography) is collected from the data, three machine learning classifiers can be used to judge the classification rate of the imaginary motion respectively. The purpose is to observe whether different classifiers will cause different effects. The classification effect of the final judgment can become a valid command for the BCI (Brain Computer Interface) to make judgments.
The part of the data set improves the identification efficiency in the public data set about 5.61%; and the part of the non-public data set improves the identification efficiency about 5.93%. Therefore,it not only effectively reduces the number of data samples , but also optimizes the final identification rate.

關鍵字(中)

★ 型態學法
★ 動態時間規畫法
★ 機器學習分類器
★ 導數有效想像運動法
★ 腦機介面

關鍵字(英)

★ morphology method
★ Dynamic time warping
★ machine learning classifier
★ derivative effective imaginative movement method
★ brain-computer interface

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VIII
表目錄 IX
第一章緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 文獻回顧 3
1-3-1 事件相關去同步 ……………………………………………… 4
1-3-2 尖波腦波偵測方法…………………………………………… 5
1-3-3 常用分類器…………………………………………………...12
1-4 內容大綱 14
第二章腦波資料 15
2-1 大腦功能區域 15
2-2 腦波頻段特性 15
2-3 EEG資料庫 17
2-3-1 BCI 競賽資料庫 17
2-3-2 想像運動非公開資料庫……………………………………….18
2-3-3 自行錄製資料庫 18
第三章演算法介紹 20
3-1 想像運動區間最佳EEG訊號起始點偵測 20
3-2 導數尖波法…………………………………………………………...25
3-3 型態學法 32
3-4 最佳EEG訊號起始點偵測………………………………………....39
3-5 動態時間規劃驗證有效偵測………………………………………..44
3-6 有效想像運動偵測後分類…………………………………………..51
3-6-1 支持向量機 51
3-6-2 線性判別分析 53
3-6-3 隨機森林……………………………………………… …….56
3-7 機率方法實踐………………………………………………………..57
3-7-1 窗口步長隨機方法…………………………………………...57
3-7-2 隨機樣本分類方法…………………………………………...59
第四章偵測最佳EEG訊號實驗結果與探討 60
4-1 有效想像運動分類架構 60
4-2 演算法架構 61
4-3 實驗結果 63
4-3-1 BCI競賽資料庫 64
4-3-2 非公開想像資料庫 67
4-3-3 演算法架構分類準確度比較 71
4-3-3-1 BCI競賽資料庫……………………………………..71
4-3-3-2 非公開想像資料庫…………………………………74
4-3-3-3 比較公開及非公開資料庫…………………………76
4-3-4 驗證有效腦波資料庫………………………………………...77
4-3-5 時間序列有效腦波偵測方法比較…………………………...77
4-3-6 機率方法實踐結果…………………………………………...80
第五章偵測想像運動實驗結果與應用 83
5-1 系統運算架構 83
5-2 狀態偵測閥值訓練與測試 85
5-3 測試結果 87
第六章結論與未來展望 89
6-1 結論 89
6-2 未來展望 89
參考文獻 90

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指導教授

徐國鎧

審核日期

2021-8-16

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