与特定运动行为相关的脑电信号特征的提取与识别是脑-机接口研究中的关键之一。
Extraction and recognition of features of brain signal related to special movement behavior is one of the keys of BCI technology.
本文的工作重点在前端，设计了一种进行意识思维任务时脑电信号(EEG)的高辨识率二分类bci，并提出了一种应用此二分类bci进行多种工作任务识别的方法。
Here the focus is the front, designing a high classification BCI only using simple sensibilities mental task EEG signal and indication a method using this BCI to sort many kinds of tasks.
本文在对小波神经网络及其算法研究的基础上，提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。
A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented in this paper.
因此，对脑电信号的自动分类和识别就显得尤其重要了。
As a result, automatic detection and classification of seizure is essential in long term EEG monitoring.
因此，对脑电信号的自动分类和识别就显得尤其重要了。
As a result, automatic detection and classification of seizure is essential in long term EEG monitoring.
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