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.

葉尹瑄、許慧玉，2023，以事件相關腦電位探討幾何剛性變換之研究。2023 第 39 屆科
學教育國際研討會，台北：國立台灣師範大學。
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