本論文主要探討利用小波法對腦波進行濾波，並使用分類樹分 類腦波特徵。近年來用腦波來控制物體移動的應用，有越來越 廣泛的趨勢。可是由於腦波非常複雜，要將資訊從雜亂且微小 的腦波中抽取出來，是一件非常困難的事。本研究使用市售的 便宜腦波儀來搜集腦波數據，並採用小波法來進行濾波，最後 使用分類樹的方法進行特徵分類。本研究成果日後可應用至使 用腦波進行飛行器或地面載具的軌跡控制。 This thesis investigates the characteristics of brainwave using wavelet analysis method and classification and regression trees. Recently brainwave has been applied to wider and wider fields. However, it is very difficult to extract useful information from brainwave due to its complex nature. In this research we selected a commercial simple EEG to reduce the expense. Wavelet analysis is employed to analyze collected data, and using classification and regression trees to induction characteristics of brainwave. The result is applicable to navigation of ground or aerial vehicles in the future.
