eeg signals processing 脑电信号分析
eeg signals classification 脑电信号分类
Complexity Analysis of EEG Signals 脑电的复杂度分析
In most researches on EEG,the original EEG signals are analyzed. There is much interference in the original epilepsy EEG signals.
现有关于脑电信号非线性分析的研究大多是对原始的脑电信号进行分析。
参考来源 - 生物电信号处理及神经网络的混沌同步研究EEG signal is a kind of complex non-stationary signal, the feature information in EEG signals can’t be extracted sufficiently only relying on traditional signal processing methods in time domain and frequency domain.
脑电信号作为一种复杂的非平稳信号，仅仅利用传统的时域和频域分析方法很难充分提取脑电信号中的特征信息。
参考来源 - 脑电信号特征信息提取的时频分析方法及虚拟式脑电图仪的研制·2,447,543篇论文数据，部分数据来源于NoteExpress
The classification accuracy of experiment EEG signals reach 89%.
对真实脑电数据的分类正确率达到89%。
A new method for detecting epileptic spikes from EEG signals is presented in this paper.
本文提出了一种新的检测癫痫eeg棘波的方法。
Ar model based distance measurements with EEG signals are well reliable for the injury detection of the central nervous system.
基于AR模型的EEG信号距离测度方法可以较好地表征中枢神经系统损伤的情况。
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