本研究透過腦機介面（BCI）技術在幾何心像旋轉測驗中旋轉0度以及旋轉135度做二元分類，希望能夠藉此探討臺灣大學生在心像旋轉中面對不同旋轉角度（0 度、135 度）時的腦電圖訊號之異同，以深入理解不同旋轉角度對心像旋轉的影響。基於BCI 領域中通道數量越少的理論基礎，對於幾何心像旋轉測驗會推薦使用五個通道：Fz、P1、P3、P6、P8；透過腦機介面技術，本研究分析了臺灣 37 位大學生並運用小波特徵數 30Hz，在機器學習中使用5層RNN(LSTM)，在心像旋轉測驗中達到了約59.80％的良好成果；將頻率切段定義成1～4Hz、4～8 Hz、8～13 Hz、13～30Hz並在所有通道算出兩兩通道間的PLI值當作特徵，在機器學習RNN(LSTM)下會得到正確率最大值約為58.80％的結果；將PSD以及PLI兩種特徵做特徵融合，並採用遞歸特徵消除（recursive　feature elimination，RFE）做特徵選擇，在機器學習 RNN(LSTM)下會得到正確率最大值約為58.02％的結果。本研究為跨領域研究，從腦神經科學的基礎上做延伸，以腦機介面領域當作輔助分析數學教育中我們需要瞭解學生的空間能力，能夠區分學生在心像旋轉中的認知狀態差異，更理解學生在不同旋轉角度下的空間能力，期盼未來能夠用更多不同的面向來探討數學教育。

　　This study uses EEG for cognitive load classification between different angles (0 degrees and 135 degrees) of geometric mental rotation. The goal is to explore the differences in EEG signals of Taiwanese college students when facing different rotation angles (0 degrees and 135 degrees) in mental rotation tasks to gain a deeper understanding of how various rotation angles affect mental rotation.
　　Based on the theory in the BCI field that fewer channels are more effective, the study recommends using five channels for the geometric mental rotation test: Fz, P1, P3, P6, and P8. By employing BCI technology, the study analyzed data from 37 Taiwanese college students and used wavelet features at 30 Hz. Using a 5-layer RNN (LSTM) in machine learning, the study achieved a good result of approximately 59.80% accuracy in the mental rotation test. When the frequency bands were defined as 1–4 Hz, 4–8 Hz, 8–13 Hz, and 13–30 Hz, and the PLI values between every two channels were calculated as features, the maximum accuracy achieved under the RNN (LSTM) machine learning was about 58.80%. Combining PSD and PLI features and applying Recursive Feature Elimination (RFE) for feature selection, the maximum accuracy obtained under the RNN (LSTM) machine learning was approximately 58.02%.
　　This interdisciplinary study extends from the foundation of neuroscience, using BCI technology to assist in analyzing students' spatial abilities in mathematics education. It aims to distinguish the cognitive states of students in mental rotation and better understand their spatial abilities under different rotation angles. The hope is to explore mathematics education from more diverse perspectives in the future.

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 6
第一節 空間能力 6
第二節 相關研究之於教育 8
第三節 腦機介面 13
第四節 腦電圖資料分類的前期研究 17
第參章 研究方法 31
第一節 研究資料 31
第二節 研究模型架構 35
第三節 資料分析 36
第肆章 結果與討論 43
第一節 不同通道在小波特徵提取的分析結果 43
第二節 不同特徵提取的結果 56
第三節 EEGNet的應用結果 64
第四節 試驗平均的分析技術 65
第伍章 結論與建議 68
第一節 結論 68
第二節 未來建議 70
參考文獻 71

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