Selection of a Suitable Wavelet for Cognitive Memory Using Electroencephalograph Signal (original) (raw)
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We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
The aim of this pilot study was to select the most similar mother wavelet function and the most efficient threshold in order to use with wavelet basis function for the human brain electrical activity during working memory task. A 60 seconds was recorded from the scalp using the Electroencephalography (EEG). 19 electrodes were placed over different sites on the scalp where analyzed for one control subject and one post-stroke patients in the first week of his stroke onset. In this study, forty-five mother wavelet basis functions from orthogonal families with four thresholding methods were used. The selection of mother wavelet functions like Daubechies (db), symlet (sym) and coiflet (coif) and the thresholding methods these are sqtwolog, rigrsure, heursure and minimax are to check mother wavelet functions similarity with the recorded EEG signals during working memory task. The test have been done using four evaluating criteria, namely signal to noise ratio (SNR), peak signal to noise ratio (PSNR) mean square error (MSE) and crosscorelation method (xcorr). Symlet mother wavelet of order 9 (sym9) is the most compatible for all the 19 channels for both EEG datasets that selected to be examined and the best results have been obtained by using the rigrsure thresholding method.
Review:Wavelet transform based electroencephalogram methods
International Journal of Trend in Scientific Research and Development, 2018
In this paper, EEG signals are the signatures of neural activities. There have been many developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with eac combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of t electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR cal proposed.
Compatibility of mother wavelet functions with the electroencephalographic signal
Electroencephalographic EEG is device that gives an electrical representation of the variation in the activity of the human brain related to distinct emotions. EEG signal acquires many kind of noise when it's travel though different layer of brain. One of the most important methods that use to remove a various kind of artifacts such as inherent noise, motion artifact, and ocular artifact is wavelet transform WT. With the suitable choice of wavelet level and mother wavelet function, it is possible to remove this artifacts noise to verify and analyze the EEG signal. Mother wavelet is particularly effective for describing a various sides of nonstationary signals such as the discontinuities and repeated patterns of the recorded EEG signal. In this research, forty five potential mother wavelet functions of Daubechies smoothing function are selected and investigated to find the most similar function with EEG signals. In this paper, determining the cross correlation function (CCF), minimum mean square error (MSE) and larger signal to noise ratio (SNR) is used to find the mother wavelet that most compatible with EEG signal. All values showed that the performances of (db4) for denoising are the best out of the wavelets by examining up to 57 different signals.
A comparative study of wavelet families for EEG signal classification
Neurocomputing, 2011
Over the past two decades, wavelet theory has been used for the processing of biomedical signals for feature extraction, compression and de-noising applications. However the question as to which wavelet family is the most suitable for analysis of non-stationary bio-signals is still prevalent among researchers. This paper attempts to find the most useful wavelet function among the existing members of the wavelet families for electroencephalogram signal (EEG) analysis. The EEGs considered for this study belong to both normal as well as abnormal signals like epileptic EEG. Important features such as energy, entropy and standard deviation at different sub-bands were computed using the wavelet functions-Haar, Daubechies (orders 2-10), Coiflets (orders 1-10), and Biorthogonal (orders 1.1, 2.4, 3.5, and 4.4). Feature vectors were used to model and train the Probabilistic Neural Network (PNN) and the classification accuracies were evaluated for each case. The results obtained from PNN classifier were compared with Support Vector Machine (SVM) classifier. From the statistical analysis, it was found that Coiflets 1 is the most suitable candidate among the wavelet families considered in this study for accurate classification of the EEG signals. In this work, we have attempted to improve the computing efficiency as it selects the most suitable wavelet function that can be used for EEG signal processing efficiently and accurately with lesser computational time.
Wavelet Based Analysis of EEG Signal for Evaluating Mental Behavior
— The purpose of the research is to evaluate the different human mental behavior through Electroencephalogram (EEG) signal with time-frequency analysis by receiving information from the internal changes of brain state. The paper presents the detection of human mental states based on some salient features of EEG signal. For this purpose seven emotional states have been specified such as relax, thought, memory related, motor action, pleasant, fear, and enjoying music. Several EEG signals have been collected for these states and analyzed using discrete wavelet transform. The discrete wavelet transform (DWT) is used to extract different significant features from the analyzed signal by computing the subband coefficients and applying statistical measures on them. Among various statistical measures maximum and minimum value, mean and standard deviation of wavelet coefficients in each subband are chosen which indicate the dispersion in different mental states and help to evaluate them. The analyzed results are compared with the spectrum analysis. It is found that wavelet analysis provides more effective way in the functioning of the brain to study of mental behavior in compare with Fourier analysis.
Analysis of EEG signal by Pattern Recognition methods using Wavelets
World Congress on …, 2007
The EEG signal is the most complicated signal having the low amplitude which makes it difficult for analysis. The signal properties of the EEG can be enhanced by the usage of wavelets which performs the much closer analysis of the signal. The different waveforms like alpha, beta, theta, gamma and delta can be studied and related with the abnormalities. The Digital Signal Processor (DSP) can be used to perform the fast and effective mathematical operations by taking the signal after it is transformed by the wavelets. The EEG signals are fed in to the processor after the wavelet transform has been performed in the form of small wavelets and they can be analyzed using the DSP. The features of the signals are extracted and the system has to be trained to classify the pattern of the signal and correlate with the predefined features. For this purpose the Vector Quantization (VQ) method is followed by taking the coefficients of the wavelet transformed vectors and then identifying the particular wave pattern by comparing with the template EEG which is extracted from a normal subject. The code is written for the processor to activate particular ports based on the interrupts and correspondingly transfer through the Direct Memory Access (DMA) .The signal is transmitted through the codec which processes the signal and passes in to the processor based on the priority of the signal. The EEG signal is decomposed in to smaller wavelets and the Coefficients are taken then the feature vectors are extracted and the vectors are transmitted in to the VQ network which offers the advantages like data compression and identification of the feature vectors. The matching techniques can be adjusted to allow the required percentage of match to be obtained between the signals. The inputs are given as the six different types of EEG waveforms and this signal can be compared with the trained features of the normal EEG and determine the corresponding matching between these signals and predict the most possible pathology of the corresponding EEG signal. This is done most effectively in the DSP processor as it involves many calculations. Matlab is also used for special purposes related to matrix and vector operations. This method proves to be useful in determining specific pathologies related to brain and give the physicians an initial idea about the brain.
EEG Data Sets Signal Processing Using Wavelet Transforms
Sensing is fundamental to all measurements, and its quality depends on many factors such as size, material used, etc. Physiological sensors measure core body temperature, ambulatory blood pressure, blood oxygen etc. Sensitive medical equipment
Continuous Wavelet Transform EEG Features of Alzheimer’s Disease
Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperativ, 2012
We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
An electroencephalogram (EEG) is an electrical signal in microvolts captured noninvasively from the brain, which provides important and unique information about the brain. The frequency of an EEG signal lies between 0 and 100 Hz. Decomposition of an EEG signal into various bands such as alpha, beta, delta, theta, and gamma is essential in seizure-related studies. EEGs play a key role in the diagnosis of epileptic seizures and neurological disorders. In this paper, multiple wavelet families for decomposition and reconstruction are explored and are compared based on risk functions and reconstruction measures. While dealing with the wavelets it is a difficult task to choose the correct/accurate wavelet for the given biosignal analysis. Various statistical properties were studied by the authors to check the suitability of various wavelets for normal and diseased EEG signal decomposition and reconstruction. The methodology was applied to 3 groups (63 subjects) consisting of both sexes and aged between 1 and 80 years: 1) normal healthy subjects, 2) patients with focal seizures, and 3) patients with generalized seizures. Our result shows that the Haar and Bior3.7 wavelets are more suitable for normal as well as diseased EEG signals, as the mean square error, mean approximate error, and percent root mean square difference of these wavelets are much smaller than in other wavelets. The signal-to-error ratio for Haar and Bior3.7 was much higher than in any other wavelet studied.