大腦人機介面是一種使用腦波與機器溝通的一種技術，透過非侵入式或侵入式的方式量測到大腦的腦波訊號，並使用大腦人機介面轉換成使用者所要控制的指令。腦波可依其頻率及特性分為α、β、θ、δ、μ等節律，其中大腦的μ(8-12 Hz)頻帶在運動想像時或實際運動時會降低其能量大小，因此可以觀察此能量的變化，計算8-12 Hz能量大小並將結果分為兩類，分別是放鬆與想像狀態，將此兩種狀態對應於LED的暗與亮，做為腦波控制的輸出應用。本論文研究重點在於自行開發基於DSP晶片之腦波分析系統，並用於分析大腦於運動想像下的狀態，觀察其頻譜能量大小，進行腦波訊號的特徵值分類，達到可即時使用腦波控制的功能。期盼本研究可幫助行動不便的人，使他們能使用腦波訊號，就可以達到與外界溝通、自主行動等目的以達到提升生活品質。

The brain-computer interface (BCI), which facilitates communication between human and a machine, can use noninvasive or invasive methods to record electroencephalogram (EEG) signals. The BCI transforms a user’s EEG signals into commands that can be utilized to control a computer or other devices. The EEG signals can be distinguished by their frequencies and characteristics, namely, the α, β, θ, δ and μ rhythms. The EEG activity in the μ (8-12 Hz) frequency bands decreases in amplitude during real or imagined movement, such as imagined movement of the right hand, left hand, or foot. Discriminating between two brain states, such as that associated with imagined right-hand movement and that associated with a relaxed brain, can correspond to the on and off states of a light-emitting diode (LED). This can be used for real-time control with BCI. This work analyzes the characteristic values of EEG by power spectrum method using a self-developed signal analysis system based on a digital signal processor (DSP). The research results can be used to help paralyzed people.

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