壓力與疲勞是人的一種生心理現象，壓力太大或太過勞累都易使身體出現異樣。本論文提出使用腦波訊號(Electroencephalography, EEG)、心率變異性(Heart Rate Variability, HRV)，搭配視覺模擬量表(Visual Analogue Scales, VAS)，或結合 EEG 與 HRV，建立主體間模型(Inter-Subject Model)和主體內模型(Intra-Subject Model)，再利用機器學習技術與統計，來分析健康受測者的壓力與疲勞。
本論文的研究結果顯示，由N-back Test 分數與VAS 未經基準線校正之相關性分析得知，分數與VAS 無相關；由EEG 特徵與VAS 未經基準線校正之相關性分析得知，壓力與VAS 的相關性比疲勞高，故此實驗流程以誘導出壓力狀態的成分居多。而使用EEG 特徵的Alpha Power 與Theta / Alpha Ratio(TAR)的分類均能達90%以上，EEG 特徵與壓力的VAS 相關性達到高相關，VAS 壓力值(放鬆與遊戲)的顯著性結果呈顯著上升，以上三點的分類與統計結果均比HRV 出色許多，故使用EEG 會比HRV 更能偵測壓力。

Stress and fatigue are a kind of psychological phenomenon in human body. In this paper, we propose to use Electroencephalography (EEG), Heart Rate Variability (HRV) and Visual Analogue Scales (VAS), or combine EEG and HRV to build an Inter-Subject Model and Intra-Subject Model, and then use machine learning technology and statistics to analyze the stress and fatigue of healthy subjects.
The results of this paper showed that the correlation between N-back test scores and VAS without baseline correction was not correlated with VAS, and the correlation between EEG features and VAS without baseline correction was higher than that of fatigue, so the experimental process induced more stress states. The classification of
Alpha Power and Theta / Alpha Ratio (TAR) using EEG features were both above 90%, and the correlation between EEG features and stress force scale was high. Therefore, the use of EEG is better than HRV in detecting stress.

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