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Description: 脑电特征分析与提取，很好的区分正常与异常脑电-EEG characteristics analysis and extraction well to distinguish between normal and abnormal EEG
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Size: 1700864 |
Author: 璀璨 |
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Description: 脑电信号的提取和特征分类还有滤波处理，适用于脑机接口技术中-EEG extraction and feature classification filtering process, applied to brain-computer interface technology
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Size: 1262592 |
Author: 郭鹏飞 |
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Description: EEg信号去噪，使用阈值法实现，并对去噪前后的信号完成比较，以实现去噪-failed to translate
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Size: 1024 |
Author: 马孝龙 |
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Description: Coherence anlaysis between ECG signal and EEG signal
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Size: 154624 |
Author: Jun-Su |
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Description: Mutual information analysis of the EEG in patients with AD
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Size: 290816 |
Author: Jun-Su |
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Description: 关于脑电信号分析的几篇pdf资料，感兴趣的朋友可以看下！-EEG analysis on several pdf information, interested friends can look!
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Size: 3120128 |
Author: Gaoqx |
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Description: 《脑电信号分析方法及其应用》书中示例程序，书的附录中有该程序列表-EEG analysis method and its application（the book program）
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Size: 25600 |
Author: 权诚 |
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Description: 可以用FFT频谱对脑电信号进行提取。我们可以利用提取出的各个波段脑电信号，来诊断一些脑部疾病或者对大脑组织的电活动及大脑的功能状态进行分析。
1.将实验测得的脑电数据文件转换为文本文件(已经过50Hz陷波)， 获得在Matlab 平台上可直接使用的脑电信号数据，即0661.txt。
2.在Matlab中导入数据，并提取Fp1通道的脑电信号，通过FFT变换对α，β，θ，δ波段进行提取，并做FFT逆变换变到时域。
3.计算各个波段的功率谱。-FFT spectrum can be extracted on EEG. We can use the extracted EEG each band to diagnose some brain disorders or brain tissue on the brain' s electrical activity and functional status were analyzed. One would experimentally measured EEG data file into a text file (has been 50Hz notch), obtained in Matlab platform can be used directly EEG data, that 0661.txt. (2) import data in Matlab and extract Fp1 channel EEG by FFT transform α, β, θ, δ bands were extracted, and do FFT inverse transform variable to the time domain. 3 calculate the power spectrum of each band.
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Size: 3391488 |
Author: nuaa030840105 |
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Description: 本科毕业时做的男女左右运动时提取脑电信号的功率谱，是分频提取的，也就是左、右手运动是的不同特征，程序简单易懂，还附有脑电信号、程序说明-Graduate men and women to do about movement of EEG power spectrum is divided extraction, that is, the left and right movement is different features, the program is simple to understand, but also with EEG description of the procedures
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Size: 16687104 |
Author: 程窦华 |
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Description: 脑电特征提取的的matlab算法EEG feature extraction-EEG feature extraction algorithm matlab EEG feature extraction
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Size: 1024 |
Author: 孙会文 |
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Description: data for EEG Epileptic
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Size: 2897920 |
Author: zahra adibi |
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Description: EEG pattern matching for controlling object -EEG pattern matching for controlling object .....................................................................................
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Size: 156672 |
Author: kalanchiya |
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Description: 脑电信号的硬阈值和软阈值去噪算法，是对脑电信号消噪的基本消噪能够成功取得脑电消噪的目的，为脑电信号的特征提取做好充分准备-EEG hard threshold and soft thresholding algorithm is the basic noise cancellation EEG de-noising can successfully achieve the purpose of de-noising EEG, EEG feature extraction is fully prepared
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Size: 4096 |
Author: dingtong |
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Description: ue to the volume conduction multichannel electroencephalogram (EEG) recordings
give a rather blurred image of brain activity. Therefore spatial filters are
extremely useful in single-trial analysis in order to improve the signal-to-noise
ratio. There are powerful methods from machine learning and signal processing
that permit the optimization of spatio-temporal filters for each subject in a data
dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here
we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular
method in brain-computer interface (BCI) research. Apart from reviewing several variants of
the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance,
briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing
in our studies of the Berlin BCI (BBCI) project.
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Size: 1248256 |
Author: fariba |
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Description: 脑电信号提取及特征分析 生物医学工程及信号处理专业可以-EEG feature extraction and analysis
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Size: 147456 |
Author: rong |
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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.
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Size: 417792 |
Author: ARUNA RAJAN |
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Description: Active Segment Selection Method in EEG Classification Using Fractal Features
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Size: 153600 |
Author: azarakhsh |
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Description: Classification of ictal and seizure-free EEG signals using fractionallinear prediction
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Size: 374784 |
Author: azarakhsh |
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Description: EEG Seizure Analysis Using Fractal
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Size: 385024 |
Author: azarakhsh |
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Description: 用于观察脑电信号全通道数据和多个单通道数据，并且输出相对颅内压参数。函数通过读取nt后缀脑电图文件，并生成GUI界面，可选择全通道观察所选文件脑电信号，也可以选择包含原始EEG脑电信号，40hzEEG，80hz肌电干扰和提取出的40hzEEG信号的多个单通道观察，并且生成颅内压参数。-Used to observe all channels of EEG data and multiple-channel data, and output parameters relative intracranial pressure. 40hzEEG signal function by reading nt suffix EEG file and generates a GUI interface, you can all channels of EEG observe the selected file, or you can choose to include original EEG EEG, 40hzEEG, 80hz EMG interference and extracted a plurality of single-channel observation, and generates an intracranial pressure parameters.
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Size: 24550400 |
Author: 吴昊 |
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