This document presents a project on wavelet based feature extraction of electroencephalography (EEG) signals. It discusses using wavelet transforms as an alternative to discrete Fourier transforms for feature extraction from EEG data. The objectives are to improve quality of life for those with disabilities through neuroprosthetics applications of brain-computer interfaces. Wavelet transforms provide advantages over short-time Fourier transforms like multi-resolution analysis and the ability to analyze non-stationary signals. The document outlines the methodology, which includes EEG signal acquisition, wavelet decomposition, coefficient computation, and signal reconstruction in MATLAB.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
An electrogastrogram (EGG) is a graphic produced by an electrogastrograph, which records the electrical signals that travel through the stomach muscles and control the muscles' contractions. An electrogastroenterogram (or gastroenterogram) is a similar procedure, which writes down electric signals not only from the stomach, but also from intestines.
APPLICATION OF DSP IN BIOMEDICAL ENGINEERINGpirh khan
DSP IS NOW A MAJOR BRANCH OF ENGINEERING AND OFTEN USED IN MANY FIELDS. THE PRESENTATION DEALS WITH APPLICATION OF DSP IN BIOMEDICAL ENGINEERING FIELD.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
This slide will provide a tutorial for preprocessing of fMRI data. The step-by-step process will be provided.
visit my website for more information:
http:/skyeong.net
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
An electrogastrogram (EGG) is a graphic produced by an electrogastrograph, which records the electrical signals that travel through the stomach muscles and control the muscles' contractions. An electrogastroenterogram (or gastroenterogram) is a similar procedure, which writes down electric signals not only from the stomach, but also from intestines.
APPLICATION OF DSP IN BIOMEDICAL ENGINEERINGpirh khan
DSP IS NOW A MAJOR BRANCH OF ENGINEERING AND OFTEN USED IN MANY FIELDS. THE PRESENTATION DEALS WITH APPLICATION OF DSP IN BIOMEDICAL ENGINEERING FIELD.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
This slide will provide a tutorial for preprocessing of fMRI data. The step-by-step process will be provided.
visit my website for more information:
http:/skyeong.net
In this study,
We propose a EEG analysis model using a nonlinear oscillator with one degree of freedom.
It doesn’t have a random term.
our study method identifies six model parameters experimentally.
Here is the detail: https://kenyu-life.com/2018/11/03/modeling_of_eeg/
Created by Kenyu Uehara
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
A mathematical model of two phase, (One phase is Newtonian and other is non-N...iosrjce
In the present paper we have formulated the renal blood flow along the capillaries in case of renal
disease Diabetes . keeping in the view the nature of renal circulatory system in human body. P.N.Pandey and
V.Upadhyay have considered the blood flow has two phased one of which is that of red blood cells and other is
plasma. According to Fahreaus-Lindqvist effect the blood flow in two separated layers while passing through
capillaries. The plasma layer which flows along the surface of the capillaries contains almost no blood cells.
The second layer the core layer containing blood cells which flows in plasma along the axis of capillary. We
have collected a clinical data in case of Diabetes for hematocrit v/s blood pressure. The graphical presentation
for particular parametric value is much closed to the clinical observation. The overall presentation is in
tensorial form and solution technique adapted is analytical as well as numerical. The role of hematocrit is
explicit in the determination of blood pressure drop in case of renal disease Diabetes
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Diabetic retinopathy also known as diabetic eye disease, is when damage occurs to the retina
due to diabetes. It can eventually lead to blindness. By analyzing and detecting vasculature structures
in retinal image the diabetes can be detected in advanced stages by comparing its states of retinal
blood vessels. In blood vessel classification approach computer based retinal image analysis can be
used to extract the retinal image vessels. Stationary wavelet transform (SWT) are used to extract the
features from the fundus image and classification can be performed using Support Vector
Machine(SVM). SVM has become an essential machine learning method for the detection and
classification of particular patterns in medical images. It is used in a wide range of applications for its
ability to detect patterns in experimental databases. If the vessels are present, then it is extracted by
using segmentation. Mathematical morphology and K-means clustering is used to segment the vessels.
To enhance the blood vessels and suppress the background information, smoothing operation can be
performed on the retinal image using mathematical morphology. Then the enhanced image is
segmented using K-means clustering algorithm to detect the diseases easily.
Image Resolution Enhancement Using Undecimated Double Density Wavelet TransformCSCJournals
In this paper, an undecimated double density wavelet based image resolution enhancement technique is proposed. The critically sampled discrete wavelet transform (DWT) suffers from the drawbacks of being shift-variant and lacking the capacity to process directional information in images. The double density wavelet transform (DDWT) is an approximately shift-invariant transform capturing directional information. The undecimated double density wavelet transform (UDDWT) is an improvement of the DDWT, making it exactly shift-invariant. The method uses a forward and inverse UDDWT to construct a high resolution (HR) image from the given low resolution (LR) image. The results are compared with state-of-the-art resolution enhancement methods.
Bring Healthcare to fingertips - How Apps changed Medical IndustryMike Taylor
With the help of a latest technologies & smartphone, Mobile Apps becomes vital part of everyone’s life. As Mobile medical apps have become a prominent part of many doctors’ practices. So going for Medical Application development will put Healthcare professionals in to WIN-WIN situation. To get complete Healthcare IT Solutions visit: http://www.brainvire.com/healthcare/
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
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journal of engineering, online Submission
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
Biomedical Signals Classification With Transformer Based Model.pptxSandeep Kumar
Seizures are caused by abnormal neuronal activity and are a common chronic brain disease. It is estimated that around 60 million people have epileptic seizures worldwide. Having an epileptic seizure can have serious consequences for the patient. Surface electroencephalogram (EEG) is a non-intrusive method frequently used to detect epileptic brain action. Nevertheless, graphic inspection of the EEG is biased, time-taken, and tedious for the neurologist. This paper proposes an automatic epileptic seizure classification technique using transformer based deep learning. Fast Fourier Transform (FFT) is first used to extract features from EEG data, and features are then used as inputs to a classifier for selection and classification. The suggested technique successfully gave 100% accuracy in the tested EEG reading. The effectiveness of the proposed method could vary across different EEG databases.
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
ENERGY COMPUTATION FOR BCI USING DCT AND MOVING AVERAGE WINDOW FOR NOISE SMOO...IJCSEA Journal
Brain computer interface (BCI) is a fast evolving field of research enabling computers and machines to be directly controlled by the human neural system. This enables people with muscular disability to directly control machines using their thought process. The brain signals are recorded using Electroencephalography (EEG) and patterns extracted so that the BCI system should be able to classify various patterns of brain signal accurately to perform different tasks. The raw EEG signal contains different kinds of interference waveforms (artifacts) and noise. Thus raw signals cannot be directly used for classification, the EEG signals has to undergo preprocessing, to remove artifacts and to extract the right attributes for classification. In this paper it is proposed to extract the energies in the EEG signal and classify the signal using Naïve Bayes and Instance based learners. The proposed method performs well for the two class problem in the multiple datasets used..
Brain-computer interface of focus and motor imagery using wavelet and recurre...TELKOMNIKA JOURNAL
Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
Wavelet Based Feature Extraction Scheme Of Eeg Waveform
1. ANNA UNIVERSITY: CHENNAI 600 025
MAY 2012
DEPARTMENT OF ELECTRONICS AND
COMMUNICATION ENGINEERING
PROJECT VIVAVOCE
2. WAVELET BASED FEATURE
EXTRACTION SCHEME OF
ELECTROENCEPHALOGRAPHY
PRESENTED BY UNDER THE GUIDANCE OF
E.ARUNA-12708106004 MR.C.E.MOHAN KUMAR, M.E
M.S.R.PUNEETHA CHOWDARI-12708106043 ASSISTANT PROFESSOR
B.SASI KALA-12708106050 ECE DEPARTMENT
N.SHANTHA PRIYA-12708106052
3. ABSTRACT
The Electroencephalogram (EEG) is a neuronal activity that represents the
electrical activity of the brain.
The specific features of EEG are used as input to Visual Evoked Potential
(VEP) based Brain-computer Interface (BCI) or self paced BCIs (SBCI)
for communication and control purposes.
This project proposes scheme to extract feature vectors using wavelet
transform as alternative to the commonly used Discrete Fourier Transform
(DFT).
The selection criterion for wavelets and methodology to implement
decomposition procedure, coefficient computation and reconstruction
methods are presented here using MATLAB software tool.
4. OBJECTIVES
To improve quality of life for those with severe neuromuscular disabilities
and aimed at restoring damaged hearing, sight and movement of muscles
by neuro-prosthetics applications based brain computer interface.
To investigate the feasibility of using different mental tasks as a wide
communication channel between neuro-diseased people and computer
systems.
To achieve the proper and efficient feature extraction algorithms can
improve the classification accuracy and to overcome the resolution
problem and localization of artifact components in time and frequency
domain.
6. INTRODUCTION
In human physiological system, Amyotrophic Lateral Sclerosis (ALS) is a
progressive neuronal-degenerative disease that affects nerve cells which
are responsible for controlling voluntary movement.
A Brain Computer Interface (BCI) or Brain Machine Interface (BMI) has
been proposed as an alternative communication pathway, bypassing the
normal cortical-muscular pathway.
BCI is a system that provides a neural interface to substitute for the loss of
normal neuronal-muscular outputs by enabling individuals to interact with
their environment through brain signals rather than muscles.
7. BRAIN COMPUTER INTERFACE
Direct connection between the brain and a computer without using any of
the brains natural output pathways.
Neural activity of the brain cells are recorded and these signals are given
as drive to applications.
Read the electrical signals or other manifestations of brain activity and
translate them into a digital form.
8. BRAIN COMPUTER INTREFACE
WORKING
Blocks of Brain-Computer Interface
EEG Signal Acquisition
Signal Preprocessing
Feature Extraction
Signal Classification
9. LITERATURE REVIEW
The history of brain–computer interfaces (BCIs) starts with Hans Berger's
discovery of the electrical activity of human brain and the development of
electroencephalography (EEG).
Electroencephalography (EEG) is the most studied potential non-invasive
interface, mainly due to its fine temporal resolution, ease of
use, portability and low set-up cost.
Research on BCIs began in the 1970s at the University of California Los
Angeles (UCLA).
The field of BCI research and development has since focused primarily
on neuro-prosthetics applications that aim at restoring damaged
hearing, sight and movement.
10. LITERATURE REVIEW (CONT.)
Invasive BCIs: Implanted directly into the grey matter of the brain during
neurosurgery.
Partially invasive BCIs: Devices are implanted inside the skull but rest
outside the brain rather than within the grey matter.
Non-invasive BCIs: Non-invasive neuro-imaging technologies as
interfaces.
Lawrence Farwell and Emanuel Donchin developed an EEG-based brain–
computer interface in the 1980s.
11. FEATURE EXTRACTION
Due to stimulus in various sense organs , the responses is created in the
surface of the brain in the form of wavelets (evoked potentials).
These potentials is are the sum of the responses due to desired (EEG
waveforms) and undesired stimulus (EMG and EOG waveform).
From these responses a desired response is extracted which is called
feature. The whole process is called Feature Extraction.
This feature is given as a input or driving signal to the application to make
it work.
12. EXISTING SYSTEM
FOURIER TRANSFORM:
Breaks down a signal into constituent sinusoids of different frequencies.
Transform the view of the signal from time-base to frequency-base.
Only analyze the stationary signals but not the non stationary signals.
It can analyze the continuous signal with uniform frequency.
j t
F f t e dt
13. EXISITING SYSTEM
SHORT TIME FOURIER TRANSFORM
To analyze small section of a signal, Denis Gabor (1946), developed a
technique based on the FT and using windowing.
A compromise between time-based and frequency-based views of a signal.
Both time and frequency are represented in limited precision. The
precision is determined by the size of the window.
Window size is fixed.
14. DRAWBACKS OF EXISTING SYSTEM
Unchanged Window and frequency of the signal should be fixed.
Localization of artifact components and transients is not accurate.
Provides a signal which is localized only in frequency domain not in time
domain.
Signal is assumed to be stationary.
FT cannot locate drift, abrupt changes, beginning and ends of events
Does not provided Multi-resolution analysis.
Dilemma of Resolution
Wide window : poor time resolution
Narrow window : poor frequency resolution
15. PROPOSED SYSTEM
WAVELET TRANSFORM:
It is a mathematical tool for processing and analyzing the EEG signals
and to localize the artifact component in it.
An alternative approach to the Fourier transform to overcome the
resolution problem.
It is used to localize the spikes, spindles, ERP‟s.
It can analyze non-stationary signals.
16. PROPOSED SYSTEM
Basic Idea of DWT: To provide the time-frequency representation.
Wavelet
Small wave
Means the window function is finite length
Mother Wavelet
A prototype for generating the other window functions
All the used windows are its dilated or compressed and shifted versions.
17. MULTI RESOLUTION ANALYSES
It is a ability to disintegrate the signal components into fine and coarse
elements.
It is also defined as ability to extract the fine components from the signals.
Analyze the signal at different frequencies with different resolutions.
Good time resolution and poor frequency resolution at high frequencies.
Good frequency resolution and poor time resolution at low frequencies.
More suitable for short duration of higher frequency; and longer duration
of lower frequency components.
18. WAVELET TRANSFORM
ADVANTAGE OF WAVELET ANALYSIS:
It permits the accurate decomposition of neuro-electric waveforms like
EEG and ERP into a set of component waveforms called detail functions
and approximation coefficients.
It provides flexible control over the resolution with which neuro-electric
components and events can be localized in time, space and scale.
Wavelet transform can analyze the discontinuous signal with variable
frequencies.
It can analyze the non stationary waves.
It provides multi resolution.
19. WAVELET TRANSFORM
ADVANTAGE OF WAVELET ANALYSIS:
Wavelet representation can indicate the signal without information loss.
Through two pass filters, wavelet representation can reconstruct the
original signal efficiently.
Compared with Fourier transform, wavelet is localizable in both frequency
domain and space domain.
Wavelet representation provides a new way to compress or modify images.
For High frequencies it uses narrow window for better resolution and for
Low frequencies it uses wide window for bringing good resolution.
20. CONTINUOUS WAVELET TRANSFORM
The sum over the time of the signal convolved by the scaled and shifted
versions of the wavelet.
It‟s slow and generates way too much data. It‟s also hard to implement.
The continuous wavelet transform uses inner products to measure the
similarity between a signal and an analyzing function.
1 * t b
C (a, b; f (t ), (t )) f (t ) dt
a a
21. CONTINUOUS WAVELET TRANSFORM
STEP 1: Take a Wavelet and compare it to a section at the start of the original signal.
STEP 2:
Calculate a number, C, that represents how closely correlated the wavelet is
with this section of the signal. The higher C is, the more the similarity.
24. DISCRETE WAVELET TRANSFORM
Wavelet transform decomposes a signal into a set of basis functions.
these basis functions are called wavelets.
Wavelets are obtained from a single prototype wavelet y(t) called mother
wavelet by dilations and shifting:
1 t b
a ,b (t ) ( )
a a
where a is the dyadic scaling parameter and b is the dyadic shifting
parameter
25. DISCRETE WAVELET ANALYSIS
(Cont.)
WAVELET CO-EFFICIENT:
At the large scale, the wavelet is aligned with the beginning of the EEG
waveform and the correlation of the wavelet shape with the shape of the
EEG waveform at that position is computed.
The same wavelet is then translated (moved) a small amount to a later
position in time, bringing a slightly different portion of the EEG waveform
a new wavelet coefficient is computed.
Whenever the wavelet shape matches the overall shape of the ERP, a large
wavelet coefficient is computed, with positive amplitude if the match is
normal and negative amplitude if the match is polarity inverted.
26. DISCRETE WAVELET ANALYSIS
(Cont.)
Conversely, when the shape match is poor, a small or zero wavelet
coefficient is computed.
At the small scale, the process of computing wavelet coefficients is the
same. The only difference is that the wavelet is contracted in time to bring
a different range of waveform fluctuations into the „„view” of the wavelet.
27. HAAR WAVELET
It is a type of Discrete Wavelet function and sequence of rescaled square
shaped functions.
Scaling function Φ (father wavelet)
Wavelet Ψ (mother wavelet)
These two functions generate a family of functions that can be used to
break up or reconstruct a signal
The Haar Scaling Functions:
Translation
Dilation
28. MATCHING WAVELETS TO EEG
WAVEFORMS
The wavelet transform is free to use wavelets as its basis functions.
Wavelets have shapes that are as close as possible to the shapes of the
EEG events.
MATCHING PURSUIT:
To examine the spectral properties of a EEG waveform over segments of
different size and location.
To select a set of basis functions from a large dictionary of basis functions
that closely match the spectral properties of those regions of the EEG
waveform.
29. MATCHING WAVELETS TO EEG
WAVEFORMS (Cont.)
MATCHED MEYER WAVELETS
A method of directly designing a wavelet to match the shape of any signal
of interest.
The technique constructs a member of a flexible class of band-limited
wavelets, the Meyer wavelets, whose spectrum matches the spectrum of
any band-limited signal as closely as possible in a least squares sense.
An associated scaling function and high and low pass filters are then
derived that can be used to perform a DWT on any EEG waveform.
30. SIGNAL DECOMPOSITION
The decomposition of the signal led's to a set of Coefficients called
Wavelet Coefficients. Therefore the signals can be re-constructed as a
linear combination of wavelets functions weighed by the Wavelet
Coefficients.
Then the signal is sent through only two “sub-band” coders (which get the
approximation and the detail data from the signal).
High frequency and low scale components are know as Detail Coefficient
and Low frequency and low frequency components are known as
Approximation Coefficients.
Signal decomposed by
low pass and high pass
filters to get approx and
detail info.
31. SIGNAL DECOMPOSTION
The signal can be continuously
decomposed to get finer detail and more
general approximation, this is called
multi-level decomposition.
A signal can be decomposed as many
times as it can be divided in half.
Thus, we only have one approximation
signal at the end of the process.
Low Pass: Scaling Function, High Pass:
Wavelet Function.
32. SUB BAND CODING
h0(n) 2 2 g0(n)
y0 (n)
x ( n) Analysis Synthesis + ˆ
x ( n)
y1 (n)
h1(n) 2 2 g1(n)
H1 ( ) H1 ( )
Low band High band
0 /2
33. SUB BAND CODING (Cont.)
Halves the Time Resolution: Only half number of samples resulted.
Doubles the Frequency Resolution: The spanned frequency band halved.
Filters h0(n) and h1(n) are half-band digital filters.
Their transfer characteristics H0-low pass filter, Output is an
approximation of x(n) and H1-high pass filter, output is the high frequency
or detail part of x(n).
Criteria: h0(n), h1(n), g0(n), g1(n) are selected to reconstruct the input
perfectly.
34. RECONSTRUCTION
A process After decomposition or analysis is called synthesis.
Reconstruct the signal from the wavelet coefficients .
Where wavelet analysis involves filtering and down sampling, the wavelet
reconstruction process consists of up sampling and filtering.
For perfect reconstruction filter banks we have
ˆ
x x
In order to achieve perfect reconstruction the filters should satisfy
g 0 [ n] h0 [ n]
g1[n] h1[ n]
Thus if one filter is low pass, the other one will be high pass.
35. IMPLEMENTATION BY MATLAB
MATLAB is high-performance interacting data-intensive software
environment for high-efficiency engineering and scientific numerical
calculations.
MATLAB is based on a high-level matrix array language with control
flow statements, functions, data structures, input/output, and object-
oriented programming features.
It integrates computation, visualization, and programming in an easy-to-
use environment where problems and solutions are expressed in familiar
mathematical notation.
37. SUMMARY
SUMMARY ON ARTIFACT REMOVAL SCHEME
The performance of the system deteriorates when the EOG and EMG
artifacts contaminate the EEG signal.
The goal of this thesis is to devise a scheme that achieves efficient artifact
removal from a composite EEG signal which in turn provides lower false
positive rates for SBCI systems.
The wavelet transform explores both time and frequency information, is
expected to be a more suitable feature extractor than those which work in
the time or frequency domain only The DWT is used main tool in this
scheme.
38. SUMMARY
SUMMARY ON MONTAGE SCHEME
The performance of the scheme was tested using the signal recorded from
13 monopolar EEG signals and from 18 bipolar EEG signals.
The performance of the system based on monopolar EEG electrodes was
weak and it resulted in high false positive rates.
Bipolar montage results in superior performance to those of the monopolar
montage.
39. SUMMARY
SUMMARY ON FEATURE EXTRACTION SCHEME
These results enable to describe the characteristics of various regions of
the brain for a specific stimulus.
The wavelet based scheme efficiently demarcates the Mu and Beta
rhythms and various other frequency bands and power associated with
each frequency band.
Bi-frequency stimulation produces more noise than single frequency
stimulation and both frequencies are not always elicited. A unique feature
vector is produced by single frequency stimulation from either
fundamental or harmonic component.
40. CONCLUSION
This project presents the use of wavelet transform for a given feature
extraction associated with electrode pair.
Mathematical basis of the wavelet transform has proved that EEG analysis
based on wavelet transform coefficients can be used very efficiently for
the estimation of EEG features.
Results of EEG feature extraction can be further improved by various
methods but one of the most important problems is in the right definition
of EEG features using both its frequency-domain and time-domain
properties.
41. FUTURE SCOPE
The proposed scheme was developed and implemented to address the
shortcomings in the design of Steady State Visual Evoked Potential
(SSVEP) based BCI systems.
SSVEP based BCI systems are assistive technology devices that allow
users to control objects in their environment using their brain signals only
and at their own pace.
This is done by measuring specific features of the brain signal that pertain
to intentional control (IC) commands issued by the user.