腦機界面(brain-computer interface)的終極目標是藉由分析腦部心智活動以從人腦直接傳送訊息到電腦，來提供人類另一個與外界系統溝通的管道。腦機界面通常被描述為個人藉由控制本身的腦波訊號(electroencephalographic signals)來與外界系統通訊，而不需經由正常的腦部神經通道與肌肉的連結途徑。在我們的腦波分析系統中，使用事件相關的腦波(event-related brain potentials)來區分左右的想像手動可以顯示出：在想像手動的期間，感應運動腦皮質區(sensorimotor cortices)上方的μ波和β波分別會有與事件相關的抑制和增益(event-related desynchronization and synchronization)的特殊現象。在腦機界面的應用上，心智訊號分析的成效和可靠性十分依賴特徵萃取與表示方法的優劣。在此論文中，我們提出一系列新的方法以有效萃取、表示和選擇有貢獻於正確率的訊號特徵。這些方法包括以t分配為權重的時間頻率平面圖(t-test-weighted time-scale plot)、活動區段選擇(active segment selection)、多解析碎形特徵向量(multiresolution fractal feature vector)結合基因演算法(genetic algorithm)、類神經-模糊時間序列預測(neuro-fuzzy time-series prediction)。這些新方法與現有知名的方法比較，其實驗結果顯示在心智工作的應用上，不管使用於真正的還是想像的手動訊號資料，我們所提出的方法都能獲得較好的結果。

The ultimate objective of a brain-computer interface (BCI) is to provide humans an alternative communication channel allowing direct transmission of messages from the brain to a computer by analyzing the brain’s mental activities. The BCI is usually described that a person has the ability to communicate with others without the prerequisite of brain’s normal output pathways of peripheral nerves and muscles by controlling his own electroencephalographic (EEG) signals. Using event-related brain potentials (ERP) to discriminate left motor imagery (MI) from right MI can indicate that there are special characteristics of event-related desynchronization and synchronization (ERD and ERS, respectively) in mu and beta rhythms over the sensorimotor cortices during MI tasks. In BCI applications, the performance and reliability of mental task analysis greatly depend on the feature extraction and representation. In this dissertation, we propose a series of methods, including t-test-weighted time-scale plot, active segment selection, multiresolution fractal feature vector (MFFV) associated with genetic algorithm (GA), and neuro-fuzzy time-series prediction to effectively extract, select, and represent features for better classification accuracy. Compared to other well-known approaches, the experimental results show that the proposed methods are superior on both real finger movement and MI data for BCI applications.

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