事件相關去同步化(ERD)為腦波於時頻上隨著動作而發生能量減弱的現象，傳統上大多使用短時傅立葉轉換及小波轉換擷取此特徵，再結合人腦電腦介面(BCI)以進行應用。希爾伯黃轉換為近年來新興的訊號處理方法，具有絕佳的時頻解析度，非常適合用於分析非穩態訊號於時頻上的變化，例如腦波的ERD及其它的生理訊號。
本研究發展一套以簡化希爾伯黃轉換為基礎的即時處理腦波訊號方法，並將其應用於辨識動作相關腦波變化。接著將此方法與前人研究結合，設計使用腦波控制游標的BCI系統。兩位中風病患經由想像動作控制游標移動至命令位置，並藉此訓練控制腦波變化能力。經訓練後，第一位病患最佳可達90%的控制成功率，第二位最佳則可達80%的成功率，且兩位受測者腦波變化情形皆與預期相符。至此完成訓練控制BCI部分，接著吾人結合BCI系統與前人研究之復健機器人，設計一套復健方法：當病人可穩定地控制腦波後，使其藉由BCI控制機器人帶動動作，以進行復健療程。經腦波控制訓練後，病人可順利地藉此方法進行復健，而此結果可證明BCI結合復健機器人的可行性。

Event-related desynchronization(ERD) is a power decrease accompany with movement-related electroencephalogram(EEG) in time-frequency domain. Short time Fourier transform and wavelet transform were often used to analyze this kind of power change, and they could be utilized as brain computer interface (BCI). Hilbert Huang transform is a new method for analyzing non-stationary signals, and it can offer very precise resolution in time- frequency domain, so it is very suitable for analyzing ERD of EEG or other physiological signals. The goal of this thesis was to develop a signal processing method based on simplified HHT for real-time ERD identification.

The method was then used to construct a cursor control BCI. Two stroked patients were trained to control the cursor to move to assigned target by imaginary hand motion and to enhance their ability of controlling EEG change or ERD. After training, the first patient reached success rate of 90% and the second reached 80% and both of their EEG showed significant ERD. By integrating a lower-limb robot and the BCI system a new rehabilitation method was developed. After training, the second patient could use the BCI to move his lower limbs through the assistance of the robot. The results showed the feasibility of integrating the EEG-based BCI and rehabilitation robots.

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