Search - EEG signals

[Graph Drawing] SigShowing

Description: 显示标准16道信号的SIG格式的脑电信号，并对信号显示进行简单控制。内含测试数据源。-Show the standard 16 signals EEG SIG format, and simple control signals. Containing the test data source.
Platform: | Size: 662528 | Author: xinchong | Hits:

[assembly language] RhythmsOfTheBrain

Description: rhythms of brai, this is about EEG signals
Platform: | Size: 5161984 | Author: farzaneh | Hits:

[Bio-Recognize] EEGMATLAB

Description: 一个用matlab小波分析自发EEG信号的程序-A wavelet analysis using matlab process of spontaneous EEG signals
Platform: | Size: 7168 | Author: 陆雪琪 | Hits:

[matlab] eeg_toolbox_v2.3

Description: EEG toolbox : EEG toolbox for matlab for processing EEG signals.
Platform: | Size: 292864 | Author: shahin | Hits:

[DSP program] DigitalSignalProcessingCourseware

Description: 数字信号处理在机械制造中，基于 FFT算法的频谱分析仪用于振动分析和机械故障诊断；医学中使用数字信号处理技术对心电(ECG)和脑电(EEG)等生物电信号作分析和处理；数字音频广播(DAB)广泛地使用了数字信号处理技术。可以说，数字信号处理技术已在信息处理领域引起了广泛的关注和高度的重视。-Digital signal processing in the machinery manufacturing, FFT algorithm based on a spectrum analyzer for vibration analysis and mechanical fault diagnosis medicine use of digital signal processing technology for ECG (ECG) and EEG (EEG) and other bio-signals analysis and treatment Digital Audio Broadcasting (DAB) extensive use of digital signal processing technology. It can be said digital signal processing technology has been in the field of information processing caused by A wide range of attention and a high degree of attention.
Platform: | Size: 16074752 | Author: 尹泉 | Hits:

[IME Develop] av_sub

Description: 脑电（EEG）是一种反映大脑活动的生物电信号，由于它具有很高的时变敏感性，在采集时极易受到外界的干扰。如眼球运动、眨眼、心电、肌电等都会给真实的脑电信号加入噪声（伪迹）。这些噪声给脑电信号的分析处理带来了很大的困难。从剔除EEG中的各种伪迹到去除噪声的效果评估研究者们都提出了很多方法。本文提出matlab除各种脑电信号伪伪迹减法- As a kind of physiological signals, the Electroencephalogram（EEG）represents the electrical activity of the brain. Because of its higher time-vary sensitivity, EEG is susceptible to many artifacts, such as eye-movements, blinks, cardiac signals, muscle noise. These noises in recording Electroencephalogram（EEG）pose a major embarrassment for EEG interpretation and disposal. A number of methods have been proposed to overcome this problem, ranging from the rejection of various artifacts to the effect estimate of removing artifacts.
Platform: | Size: 1024 | Author: yangyangwang | Hits:

[Industry research] cr1122

Description: this file about CLASSIFICATION OF CHAOTIC SIGNALS USING HMM CLASSIFIERS: EEG-BASED MENTAL TASK CLASSIFICATION
Platform: | Size: 169984 | Author: Haithem Tahon | Hits:

[matlab] PCATEST

Description: pca分类程序，主要用于脑电信号的分类。具有较好的分类精度！-pca classification procedures, mainly for the classification of EEG signals. Has better classification accuracy!
Platform: | Size: 530432 | Author: 人咧 | Hits:

[DSP program] ss

Description: REMOVAL OF NOISE FROM ECG (ELECTROCARDIOGRAPHY) BY USING MATLAB. EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyography) signal, EOG (Electrooculograph) signal or ECG (Electrocardiograph) signal. These artifacts appear as noise in the recorded EEG signal individually or in a combined manner. Usually physicians are misled by these noisy signals and the EEG analysis can go wrong. This paper presents noise cancellation i.e. removal of noise signal which can be either EMG, ECG or a combination of these two artifacts from the corrupted EEG signal and also signal enhancement both using recurrent learning technique. For this purpose, we have implemented the RTRL (Real Time Recurrent Learning) algorithm, -REMOVAL OF NOISE FROM ECG (ELECTROCARDIOGRAPHY) BY USING MATLAB. EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyography) signal, EOG (Electrooculograph) signal or ECG (Electrocardiograph) signal. These artifacts appear as noise in the recorded EEG signal individually or in a combined manner. Usually physicians are misled by these noisy signals and the EEG analysis can go wrong. This paper presents noise cancellation i.e. removal of noise signal which can be either EMG, ECG or a combination of these two artifacts from the corrupted EEG signal and also signal enhancement both using recurrent learning technique. For this purpose, we have implemented the RTRL (Real Time Recurrent Learning) algorithm,
Platform: | Size: 1024 | Author: azharuddin | Hits:

[Software Engineering] bci-labview

Description: this about the brain computer interface using labview.the eeg signals are simulated and viewed here there by comuniating human brain to the computer
Platform: | Size: 2013184 | Author: archana | Hits:

[matlab] hwEEGECG_ica

Description: 关于脑电信号预处理中的从脑电信号中滤除心电信号，用的是独立分量分析的方法，编写的程序很简单明了，并画出图形进行直观的观察对比-Pretreatment on EEG signals from the brain to filter the ECG, using independent component analysis method, a program written in very simple and intuitive observation to draw graphics comparison
Platform: | Size: 2048 | Author: huangwei | Hits:

[matlab] Untitled

Description: EEG信号多分辨率分析,可以用改文件对EEG信号进行小波处理，即过滤去噪等。-EEG signals analysis
Platform: | Size: 1024 | Author: ray | Hits:

[matlab] draw1002

Description: 该程序主要用于脑电信号EEG的小波分析，进行5层小波分析重构系数-The program is mainly used in EEG signals of EEG wavelet analysis, 5 layer wavelet analysis reconstruction coefficient
Platform: | Size: 1024 | Author: 陈晨 | Hits:

[Graph Recognize] EEG-based-identification-method

Description: ：基于脑电信号的身份识别是通过采集试验者的脑部信号来进行身份认证。对于同一个外部刺激或者主体在思考同一个事件的时候，不同人的大脑所产生的认知脑电信号不同。选取与运动意识想象有关的电极后，分析不同个体在特定状况下脑电的个体差异，采用以回归系数、能量谱密度、相同步、线性复杂度多种信号处理结合方法对运动想象脑电信号进行处理来进行特征提取。组合多元特征向量并运用多层BP 神经网络对不同个体的脑电信号进行分类，并在不同的意识想象及不同数据长度、不同的波段对试验者进行识别率验证分析。结果表明，不同运动想象的平均识别率均在80 以上，其中以想象舌头运动的识别率较高，达到90.6 ，不同波段的识别率也表明意识想象的模式及相应波段对身份认别有较大的影响。-EEG-based identification to authenticate through the acquisition of experimental brain signals. For the same external stimuli, or the main thinking of the same Event, different people s brains produced by cognitive EEG. Select imagine the electrodes and movement awareness, analysis of different individuals in a particular situation brain Individual differences in electricity, the use of regression coefficients, the energy spectral density, phase synchronization, the linear complexity of a variety of signal processing combined with motor imagery EEG For feature extraction. The combination of multiple feature vectors and the use of multi-layer BP neural network to classify the EEG signals of different individuals, and in a different sense of imagination and a different number of Length, the band on the test to verify the analysis of the recognition rate. The results show that the average recognition rates of different motor imagery in more than 80 , which to imagine the tongue The m
Platform: | Size: 551936 | Author: 王闯杰 | Hits:

[File Format] Eeg-signals

Description: 从脑电信号的分析出发,论述了频域分析、时域分析等脑电图分析中常用的信号分析方法和特点,特别介绍了Wigner分布、小波变换和匹配跟踪等时频分析方法、人工神经网络和非线性动力学方法在脑电信号分析和处理中的应用情况。 -From the analysis of eeg, discusses the frequency domain and time domain analysis analysis in the analysis of the commonly used eeg signal analysis method and characteristics, especially introduced the Wigner distribution, wavelet transform and matching trace the method of time-frequency analysis, artificial neural network and nonlinear dynamics method of eeg analysis and processing in the application.
Platform: | Size: 32768 | Author: lvxin | Hits:

[Industry research] EEG-for-biometrics

Description: Biometric recognition is the science of establishing the identity of a person using his/her physical or biological characteristics. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this report the feasibility of the EEG signals as raw materials for conducting biometric authentication of individuals is investigated. Brain responses are extracted with visual stimulation (leading to biological brain responses known as Visual Evoked Potentials) or while relaxing with the eyes closed.
Platform: | Size: 417792 | Author: ARUNA RAJAN | Hits:

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