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    題名: 應用小波轉換技術於腦機介面之研究
    Apply Wavelet Transform to Brain Computer Interface
    作者: 孫光天;吳長達;李耀全
    Koun-Tem Sun;Chang-Ta Wu;Yao-Chuan Li
    日期: 2007
    上傳時間: 2009-11-27 14:44:12 (UTC+8)
    摘要: 早期腦機介面技術,普遍以傅利葉轉換為主,近年來,小波轉換技術逐漸被採用,其特性在對於未知訊號的頻率分佈,在時間軸上可以得到很好的解析度,適合應用於腦波的不穩定訊號分析處理。再配合類神經網路非線性分辨能力,可有效分辨α波、β波。故本研究將結合這二項技術、方法,針對腦機界面研究,提出一新技術,經人體實驗測試其技術在游標二維(上、下)方向控制之正確可達到75.5%,與目前國際水準相近。
    In the previous day, Fourier is the most common method used on brain computer interface(BCI). Recently, wavelet transform widely used to BCI the ability to process the unknown signal frequency distribution, and it can obtain a better resolution at time domain. The neural network has the non-linear classification ability that can be used to classify the α and β band frequency. In this, we will combine the wavelet transform and the neural network to analyze the EEG signals.
    The experimental results show that the success rate of controlling the cursor up and down morement above 75.5%. This achievement is comparable with other related researches in the world.
    顯示於類別:[資訊處] 國際醫學資訊研討會論文集

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