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- Wireless communication of EEG signal. 脑电信号无线通信;
- Bispectral Analysis of EEG Signal during Focal Cerebral Ischemia. 局灶性脑缺血脑电信号的双谱分析
- The result of study indicates that SVM has the strong ability of classification to P300 EEG signal, and has promoted the development of BCI. 研究结果表明,支持向量机对P300脑电信号有很强的分类能力,促进了脑机接口的发展。
- The EEG signal has the characteristic of strong randomness, nonstationarity and it is also real-time signal. 摘要脑电(EEG)信号具有随机性、非平稳性和实时性,通常受到很强的噪声干扰。
- The time-frequency distribution(TFD)fits to analyze the EEG signal well since EEG is a typi -cal non-stationary signal. 由于脑电信号(EEG)是典型的非平稳时变信号,因此时频分析方法比较适用于分析和处理EEG信号。
- The CSSP algorithm is presented to classify EEG signal with small training sets. 摘要 利用公共空间频率模型算法实现较少训练数据的脑电识别。
- So the analysis and process of the EEG signals are always attended. 因此对脑电信号的分析和处理一直是人们努力研究的领域。
- This method has been applied to the interference pulse dete ction in EEG signal. 我们用该方法对实测脑电信号(EEG)中瞬态脉冲干扰进行检测。
- As a result, it was found that bispectrum was sensitive to discriminate the EEG signal associated to different Stroop cognitive task. 利用这种非高斯AR模型的双谱估计方法对双语Stroop认知任务的脑电信号进行了处理,发现双谱可较敏感地区别不同类型的Stroop任务。
- Sufficient results are obtained by applying this method to the spike detection of the simulation signal and the real epileptic EEG signal. 在对数值模拟的和真实的癫痫脑电信号(EEG)的仿真实验中,该方法都取得了较好的结果。
- The simulation of the designed circuit and the debug of objection can be well corroborated and the ideal EEG signal can be obtained in the system. 电路仿真和实物调试都能很好地印证设计目标,能够采集到较为理想的脑电信号。
- One is a time-frequency distribution based EMD, the other is nonlinear energy operator (NEO) based on EMD, and both of them have good results in epileptic EEG signal processing. 一个是基于EMD的时频分布,另一个是基于EMD的非线性能量算子(NEO)方法,其在癫痫脑电信号的处理中都取得了比较好的效果。
- Objective To study the difference between the nonlinear eigenvalue of epileptic patients and normal subjects for accurate identification of epileptic EEG signal. 目的分析癫痫脑电信号,观察癫痫患者与健康成人脑电的非线性特征参数的差异值以利于识别。
- Digital process of EEG signal. 脑电信号的数字处理;
- Based on the characteristic of P300 EEG signal, this paper has studied the classification of the artificial P300 signal and international standard experiment signal using Support Vector Machines (SVM). 本文针对P300脑电信号的特点,通过P300仿真信号和国际标准实验信号,对支持向量机分类识别P300脑电信号进行了研究。
- The ambulatory EEG recorder designed in this paper can continuously record 24-hour EEG signals on 18 channels. 设计的动态脑电图记录盒可以记录24h的18导动态脑电信号。
- But EEG signals recorded in the ward exposed to various noises and interference is less effective in the EEG analysis. 但是病床边采集的脑电信号易受各类噪声和干扰的影响,往往影响后面的分析效果。
- Specifically, in this paper, the following work is performed:(1) Modeling GM (1, 1) for EEG signals. 脑电信号的灰色GM(1,1)建模;
- It extracts the high frequency components related to spikes in EEG signal by EMD method. It detects the spikes by calculating the instantaneous amplitude of the high component with Hilbert transform. 这种方法将EMD分解与Hilbert变换相结合,自适应地提取原始EEG信号中包含棘波特征的高频分量。
- ObjectiveTo detect the high-order information in EEG signals by bispectral analysis based on high-order spectral techniques. 目的使用高阶谱技术建立脑电信号的双谱分析方法 ,以探讨脑电信号中蕴涵的高阶信息。