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作者(中文):邱柔禎
作者(外文):Chiu,Jou-Chen
論文名稱(中文):以 EEG 進行幾何心像旋轉與地圖視角轉換認知分類 的腦機介面開發研究
論文名稱(外文):Research on the development of brain-computer interface using EEG for classification between mental rotation and perspective-taking
指導教授(中文):許慧玉
丁志堅
指導教授(外文):Hsu, Hui-Yu
Ding, Tsu-Jen
口試委員(中文):莊鈞翔
陳建誠
鄭英豪
口試委員(外文):Chuang, Chun-Hsiang
Chen, Jian-Cheng
Cheng, Ying-Hao
學位類別:碩士
校院名稱:國立清華大學
系所名稱:數理教育研究所
學號:111198512
出版年(民國):113
畢業學年度:112
語文別:中文
論文頁數:86
中文關鍵詞:空間能力心像旋轉視角轉換腦機介面
外文關鍵詞:spatial abilitymental rotationperspective-takingBrain-Computer Interface(BCI)
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本研究旨在探索如何通過有效的前處理、特徵提取及機器學習方法之腦機介面
(Brain-Computer Interface, BCI)技術,以提高針對台灣學生在幾何心像旋轉及地圖視角
轉換任務中不同空間認知任務的分類正確率,以提供更多客觀的數據和指標,進一步
協助教師診斷學生空間能力。
利用卷積神經網絡 CNN、SVM、KNN、RNN 和 EEGNET 等機器學習架構,本研
究分析了腦波資料並進行特徵選取和通道選擇,結果顯示支持向量機 SVM 模型搭配連
續小波變換 CWT 作為特徵提取方法是各模型中表現最佳的,最高正確率可達 77.8%,
特別是 SVM 搭配 shan 小波函數,其平均正確率達到 75.1%。以及選擇有代表性的通道
進行PLI計算可以提高分類準確率,同時減少計算成本。使用小波轉換後的特徵信號進
行EEGNET 模型分類,平均正確率為75.6%高於使用原始腦波數據的70.5%。本研究提
供了一種新的評估學生空間能力的方法,為空間能力教學提供更多可能性。
This study aims to explore how effective preprocessing, feature extraction, and machine
learning methods in Brain-Computer Interface (BCI) technology can enhance the
classification accuracy of different mental rotation tasks among Taiwanese students in
geometry mental rotation and map perspective-taking tasks. The goal is to provide more
objective data and indicators to assist teachers in diagnosing students' mental rotation
abilities.
Using machine learning architectures such as Convolutional Neural Networks (CNN),
Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Networks
(RNN), and EEGNET, the study analyzed EEG data, performed feature selection, and channel
selection. The results showed that the SVM model combined with Continuous Wavelet
Transform (CWT) as the feature extraction method performed the best among all models,
achieving a maximum accuracy rate of 77.8%. Specifically, the SVM paired with the Shan
wavelet function attained an average accuracy of 75.1%. Furthermore, selecting representative
channels for Phase Lag Index (PLI) calculation improved classification accuracy while
reducing computational costs. The EEGNET model, when classifying feature signals obtained
from wavelet transform, achieved an average accuracy of 75.6%, higher than the 70.5%
accuracy using raw EEG data. This study provides a new method for evaluating students'
mental rotation abilities, offering more possibilities for mental rotation education.
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與待答問題 2
第三節 名詞釋義 3
第貳章 文獻探討 5
第一節 心像旋轉 5
第二節 視角轉換(perspective-taking) 7
第三節 心像旋轉和視角轉換比較 8
第四節 心像旋轉和視角轉換在試驗平均資料之研究結果 9
第五節 教育相關研究 14
第六節 腦機介面 15
第七節 機器學習與深度學習 17
第八節 腦電圖資料分類的前期研究 25
第參章 研究方法 28
第一節 研究資料 28
第二節 研究流程與架構 31
第三節 前處理、特徵提取與選擇 32
第四節 機器學習設計 36
第肆章 結果與討論 41
第一節 不同的特徵提取的正確率 41
第二節 不同的特徵選擇的正確率 57
第三節 不同的通道選擇的正確率 61
第四節 EEGNET 下不同模型調整的正確率 63
第五節 不同階段資料正確率比較 64
第伍章 結論與建議 70
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