本研究透過人因實驗，在不同聲音環境下紀錄受試者在休息以及專注遊玩電腦遊戲下的專注力相關之生理及心理效應，包括腦波訊號擷取、工作績效評估，以及受試者的主觀評量。並藉由訊號處理分析受試者的腦波資料，提取包含碎形維度、近似熵、希爾伯特-黃轉換(HHT)後的邊際頻譜作為分類的特徵，配合分類器如接受者操作特徵曲線(ROC curve)或支持向量機(SVM)進行不同情境間資料的訓練及分類，判別受試者在各個情境間的腦波的差異狀況。計算出分類正確率、接受者操作特徵曲線的AUC值，並且比較各種分類結果，找出適合的特徵以及分類方法。;This research uses human factors experiments to record the physical and psychological effects of the participants’ concentration during rest and playing computer games in different sound environments. The brainwave data of the participants were analyzed by fractal dimension, approximate entropy, and Hilbert-Huang transform (HHT) into the classifiers, including receiver operating characteristic (ROC curve) and support vector machine (SVM). They are used to train and classify data between different situations, and to distinguish the differences in the brainwaves of participants in each situation. By calculating the classification accuracy and the AUC value of the ROC curve and comparing various classification results, we can figure out suitable attributes and classification methods.
