在这个系统中，通信载体、信源编码、虚拟键盘的设计和脑-机接口信号的单次提取是四个最核心的问题。
There are four key issues in the system, that is communication carriers, source coding, designation of virtual keyboard, and the single-trial estimation of its message carriers.
诱发电位（EP）和事件相关电位（ERP）的单次提取是生物医学信号处理领域颇受关注的一个研究问题。
Single-trial estimation of evoked potentials(EP)and event-related potentials(ERP)is the interesting field in biomedical signal processing.
从被淹没在数十微伏的自发脑电背景中单次提取微伏级的视觉诱发电位，是脑-计算机接口的核心问题之一。
One of the key issues in a brain computer interface is to single-trial estimate the visual evoked potentials which embedded in ongoing spontaneous Electroencephalogram(EEG) background.
提出了两种在相位轮廓术中使用离散小波变换提取条纹相位的方法：单次分解法和多次分解法。
Two approaches, single decomposition approach and multi-decomposition approach, using discrete wavelet transform for extracting phase in phase profilometry are proposed.
由于小波变换在时域和频域上都具有良好的局部化特性，基于小波变换的去噪方法，可以达到从单次样本中提取视觉诱发电位的目的。
Due to the good localization feature of the wavelet transform both in time plane and in frequency plane, a wavelet-based de-noise method is presented for extracting EPs in single trails.
由于小波变换在时域和频域上都具有良好的局部化特性，本文提出了一种基于小波变换的去噪方法，以期达到从单次样本中提取视觉诱发电位的目的。
Due to the good localization feature of the wavelet transform both in time plane and in frequency plane, we present a wavelet-based denoising method for extracting EPs in single trails.
由于小波变换在时域和频域上都具有良好的局部化特性，本文提出了一种基于小波变换的去噪方法，以期达到从单次样本中提取视觉诱发电位的目的。
Due to the good localization feature of the wavelet transform both in time plane and in frequency plane, we present a wavelet-based denoising method for extracting EPs in single trails.
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