在這篇論文中我們提出了一個結合獨立成分分析(Independent Component Analysis)及小波轉換(Wavelet Transformation)於分組腦波中偵測癲癇的方法。首先，利用獨立成分分析來強化癲癇，接著以小波轉換分析並配合一個動態的門檻值來鑑定癲癇。為了評估我們提出的方法之效能，我們做了一連串的實驗來和我們提出的方法做比較，實驗結果顯示了我們提出的方法較其他的方法更能完整的找出癲癇。

In this thesis, we propose a new scheme which combines ICA with wavelet transformation on grouped EEG signals for epileptic seizure detection. The Independent Component Analysis (ICA) is adopted to enhance epileptic seizure. Then, wavelet transformation is followed with a dynamic threshold for identifying the epileptic seizure location. A series of experiments has been conducted to evaluate the proposed approach. The experimental results show that the proposed method has a superior performance than other approaches.

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