近年來，情緒誘發實驗盛行，主要情緒辨識方式分為行為模式及生理模式，行為模式有臉部辨識(Facial recognition)、語音辨識(Speech recognition)等；生理模式有腦電圖(Electroencephalogram, EEG)、心電圖(Electrocardiogram, ECG)、皮膚電反應(Galvanic Skin Response, GSR)、心率(Heart Rate, HR)，及心率變異性(Heart Rate Variability, HRV)等。在本研究藉由穿戴裝置收集受測者之腦電圖(EEG)、皮膚電反應(GSR)、心率(HR)，及心率變異性(HRV)之生理數據進行情緒誘發實驗，並應用六種情緒誘發影片，分別為恐懼(Fear)、悲傷(Sadness)、厭惡(Disgust)、憤怒(Anger)、歡喜(Joy)、驚喜(Surprise)，以及國際情感圖庫系統(IAPS, International Affective Picture System)之 54 張圖片，誘發 50 位受試者情緒表現，同時收集主觀 SAM 問券數據及客觀生理數據，進行重複實驗單因子變異數分析、成對比較，以及使用獨立成分分析(independent component analysis, ICA)降維方式，做情緒辨識預測，探討主觀 SAM 量表與客觀生理訊號之不同生理數據組合與不同演算法對情緒辨識模型的影響。從研究結果欲找出最佳情緒辨識模型，當受測者在觀看情緒影片及圖片時，藉由生理訊號預測受測者，對於情緒影片及照片情緒之分類辨識。在影片方面，以六類情緒辨識時，極限梯度提升(eXtreme Gradient Boosting, XGBoost)演算法準確率為 62%；以兩類之 正 、 負 向 情 緒 辨 識 時 , 極 限 梯 度 提 升 (eXtreme Gradient Boosting, XGBoost) 及Voting(Random Forest+ XGBoost)演算法準確率可達 97%。在圖片方面，以三類之正、負向、中性情緒圖片辨識時，使用隨機森林(Random Forest) 及極限梯度提升(eXtreme Gradient Boosting, XGBoost)演算法準確率可達 67％；以二類之正、負向情緒圖片辨識時，使用隨機森林(Random Forest, RF) 、極限梯度提升(eXtreme Gradient Boosting, XGBoost)及 Voting(Random Forest+ XGBoost)演算法準確率最高都可達 81％。在本研究第 5 章討論，經過整體模型性能比較後，又以 XGBoost 演算法最佳。本研究將可以藉由生理數據之統計分析及預測模型方式，供未來研究員作為進一步的參考。

In recent years, emotion induction experiments have become prevalent, and the main
methods for emotion recognition are behavioral and physiological. Behavioral methods include facial recognition, speech recognition, etc., while physiological methods include Electroencephalogram (EEG), Electrocardiogram (ECG), Galvanic Skin Response (GSR), Heart Rate (HR), and Heart Rate Variability (HRV). In this study,physiological data, including EEG, GSR, HR, and HRV, were collected from participants using wearable devices during emotion induction experiments. Six emotion induction videos were used, namely Fear, Sadness, Disgust, Anger, Joy, and Surprise, as well as 54 images from the International Affective Picture System (IAPS). Fifty participants were recruited, and subjective Self-Assessment Manikin (SAM) questionnaires and objective physiological data were collected to analyze the effect of different physiological data combinations and algorithms on emotion recognition models using
repeated measures ANOVA, paired comparisons, and Independent Component Analysis (ICA)
dimensionality reduction.According to the research results, the aim is to find the optimal emotion recognition model that can predict the classification of emotions experienced by participants while watching emotion-inducing videos and IAPS images based on physiological signals. In the case of emotional videos with six emotion categories, the eXtreme Gradient Boosting (XGBoost) algorithm achieves an accuracy of 62%. When it comes to distinguishing between positive and negative emotional videos, both the XGBoost and Voting (Random Forest+ XGBoost) algorithms reached an accuracy of 97%.For the three categories of IAPS images (positive, negative, and neutral), the Random Forest algorithm and XGBoost had an accuracy of 67%, and for positive and negative IAPS images, the Random Forest, XGBoost, and Voting (Random Forest +XGBoost) algorithms all achieved the highest accuracy of 81%. In the discussion of Chapter 5 of this study, after comparing the overall performance of the models, the XGBoost
algorithm is determined to be the best. The results of this study can be used as a reference on statistical analysis and predictive models of physiological data for future researchers.

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