Location:
Search - eeg e
Search list
Description: EEG brain signal anlaysis codes. Extract event-related potential (ERP) compoents from brain signal. Estimate ERP waveforms, and the assoimated single-trial latencies and amplitudes. Estimate spectrum of on-going activities. Two algoithms, dVCA and AESO, are implemeneted. Could be useful for researchers and students in biomeical engineering.
Platform: |
Size: 14336 |
Author: oasis |
Hits:
Description: 本程序通过调用matlab提供的信号分析函数对脑电信号进行分析变换，对信号处理工作具有很大帮助。-This procedure provided by calling matlab EEG signal analysis function analysis of transformations, the signal processing of great help.
Platform: |
Size: 1848320 |
Author: Bruce |
Hits:
Description: wrapper classes to easily read and plot bdf file (EEG) in Python
Platform: |
Size: 433152 |
Author: iveq nguyen |
Hits:
Description: 最近在研究脑电信号的处理 同事自己也收集了一些资料希望和大家分享-Recent studies dealing with EEG colleagues collected themselves would like to share some information
Platform: |
Size: 234496 |
Author: zhq |
Hits:
Description: 自己编写的脑电滤波程序，实现0.3到120HZ范围的滤波-I have written EEG filtering process, to achieve the filtering range of 0.3 to 120HZ
Platform: |
Size: 22528 |
Author: yy |
Hits:
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:
Description: There are five e-books in this .zip archive including EEG analysis using frequency analysis and other information about EEG analysis.
Platform: |
Size: 51368960 |
Author: Wajid |
Hits:
Description: 针对生物图像的一维无损压缩，比如ECG，EEG等，输入必须是一维数据格式。-One-dimensional lossless compression for biological signals, e.g., ECG, EEG, etc. Input must be raw data format consisting of series of signed short (2 bytes) integers.
Platform: |
Size: 30720 |
Author: 张希 |
Hits:
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.
Platform: |
Size: 1248256 |
Author: fariba |
Hits:
Description: eeg processing e books
Platform: |
Size: 1147904 |
Author: SUVODIP CHAKRABORTY |
Hits:
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:
Description: The human brain contains billions of nerve cells to communicate with each other and other cells, nerve cells are exchanged. Nerve impulses are electro-chemical nature. To record the brain s electrical signals can be of electrodes (EEG) can be used.
Platform: |
Size: 244736 |
Author: yas |
Hits:
Description: 张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature
Extraction and Applications-
Data in modern applications such as BCI based on EEG signals often contain multi-modes due to
mechanism of data recording, e.g. signals recorded by multiple-sensors (electrodes), in multiple trials,
epochs, for multiple subjects and with different tasks, conditions. Moreover, during processing and
analysis, dimensionality of the data could be augmented due to expression of the data into sparse
domain (time-frequency representation) by different transforms such as STFT, wavelets. That means
data itself is naturally a tensor, and has multilinear structures. Standard approaches which analyze
such data by considering them as vectors or matrices might be not suitable due to risk of losing the
covariance information among various modes. To discover hidden multilinear structures, features
within the data, the analysis tools should reflect the multi-dimensional structure of the data
Platform: |
Size: 2438144 |
Author: 李新会 |
Hits: