scholarly journals Incursion Model for Nomenclature of EEG Signals via Wavelet Transform

2011 ◽  
pp. 29-33
Author(s):  
P.V.Rama Raju ◽  
V.Malleswara Rao ◽  
N.Anogjna Aurora
Keyword(s):  
Wavelet Transform ◽  
Brain Disorders ◽  
Eeg Signals ◽  
First Line ◽  
Multiple Electrodes ◽  
Accurate Picture ◽  
Short Period ◽  
Stationary Signals ◽  
Scientific Decision ◽  
Eeg Signal Processing

EEG refers to the recording of the brain’s spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. In advance EEG signals used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders. The structure generating the signal is not simply linear, but also involves nonlinear contributions [7, 8, 9].These non-stationary signals are may contain indicators of current disease, or even warnings about impending diseases. This work aims at providing new insights on the Electroencephalography (EEG) fragmentation problem using wavelets [2, 5]. The present work describes a computer model to provide a more accurate picture of the EEG signal processing via Wavelet Transform [16, 17, 18, 19]. The Matlab techniques have been uses which provide a system oriented scientific decision making modal [16, 17]. Within this practice the applied signal has been compared in a sequential order with dissimilar cases in attendance in the database. Special EEG signals have been considered from Physio bank [1] and Vijaya Medical Centre, Visakhapatnam, India. Analyze the signal under consideration and renowned the holder 100% truthfully.

scholarly journals Analysis and Exposure of Parkinson’s Disease Signal in the Vicinity of Wavelet Transform

2011 ◽  
pp. 26-31
Author(s):  
P.V.Rama Raju ◽  
P.N.T.L. Durga ◽  
B.G.S. Anusha ◽  
A. Bhogeswararao ◽  
M.BalaSai Krishna ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Wavelet Transform ◽  
Neurodegenerative Disorder ◽  
Body Movement ◽  
Muscle Rigidity ◽  
Sequential Order ◽  
Accurate Picture ◽  
Scientific Decision

Parkinson's disease (PD) is a gradual progressive central neurodegenerative disorder that affects body movement and is characterized by symptoms such as muscle rigidity, resting tremors, loss of facial expression, hypophonia, diminished blinking, and akinesia [4]. This work aims at providing new insights on the Parkinson's disease fragmentation problem using wavelets [1, 2, 3]. The present work describes a computer model to provide a more accurate picture of the Parkinson's disease (PD) signal processing via Wavelet Transform [7, 8, 9, 10]. The Matlab techniques have been uses which provide a system oriented scientific decision making modal [7, 8]. Within this practice the applied signal has been compared in a sequential order with dissimilar cases in attendance in the database. Special biomedical signals have been considered from Gait in Aging and Disease Database [6] and Physio bank [5]. Analyze the signal under consideration and renowned the holder 100% truthfully.

scholarly journals EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2505 ◽  
Author(s):  
Fahd A. Alturki ◽  
Khalil AlSharabi ◽  
Akram M. Abdurraqeeb ◽  
Majid Aljalal
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Single Channel ◽  
Autism Spectrum ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.

VIGILANCE ANALYSIS BASED ON EEG SIGNALS: SEEKING FOR SUITABLE FEATURES

2010 ◽  
Vol 18 (spec01) ◽  
pp. 81-99
Author(s):  
TIAN OUYANG ◽  
HONG-TAO LU ◽  
BAOLIANG LU
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Large Set ◽  
Eeg Signal ◽  
Simulation Environment ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Eeg Signal Processing

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. EEG signals are recorded and divided into five-second long trials. From these EEG trials, we extract feature vectors containing a large set of features. Random forest is used to rank the plenty of features and select the most important ones for later classification. After dimension reduction, sample vectors are trained and classified by Support Vector Machine (SVM). The proposed framework explores different methods of EEG signal processing to discover the most suitable features for a real-time vigilance monitoring system. We investigate and compare three different kinds of features which are based on Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Fractal Dimension (FD), respectively. On datasets acquired from 5 subjects, our result shows the CWT-based features reveal the highest classification accuracy (may reach over 96%). The DWT and FD-based features are less time-consuming in computation, and also reveal good result of classification accuracy (over 90%).

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

scholarly journals EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 10584-10605 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Ammar Kamal Abasi ◽  
Sharif Naser Makhadmeh
Keyword(s):  
Wavelet Transform ◽  
Eeg Signals ◽  
Metaheuristic Methods

Cross-wavelet transform as a new prototype for classification of EEG signals

2018 ◽  
Vol 7 (3) ◽  
pp. 348-358 ◽  
Author(s):  
Priyadarshiny Dhar ◽  
Saibal Dutta ◽  
V. Mukherjee ◽  
Abhijit Dhar ◽  
Prithwiraj Das
Keyword(s):  
Wavelet Transform ◽  
Eeg Signals ◽  
Cross Wavelet Transform ◽  
Cross Wavelet

scholarly journals A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results

2019 ◽  
Vol 63 (3) ◽  
pp. 425-434 ◽  
Author(s):  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
3D Video ◽  
Support Vector ◽  
Least Squares Regression ◽  
Eeg Signals ◽  
3D Technology ◽  
Svm Classification ◽  
Brain Signals ◽  
Short Period ◽  
The Impact ◽  
The Brain

Abstract Despite the development of two- and three-dimensional (2D&3D) technology, it has attracted the attention of researchers in recent years. This research is done to reveal the detailed effects of 2D in comparison with 3D technology on the human brain waves. The impact of 2D&3D video watching using electroencephalography (EEG) brain signals is studied. A group of eight healthy volunteers with the average age of 31 ± 3.06 years old participated in this three-stage test. EEG signal recording consisted of three stages: After a bit of relaxation (a), a 2D video was displayed (b), the recording of the signal continued for a short period of time as rest (c), and finally the trial ended. Exactly the same steps were repeated for the 3D video. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. After testing all the EEG frequency bands, delta and theta were extracted as the features. Partial least squares regression (PLSR) and Support vector machine (SVM) classification algorithms were considered in order to classify EEG signals obtained as the result of 2D&3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions.

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