hi, i saw your presentation and its helping me a lot to understand the use of wavelets for feature extraction. Is there a posibility for you to upload or send me the MATLAB examples? im working on a similar project using LabVIEW Instead. Thank you!
Wavelet Based Feature Extraction Scheme Of Eeg Waveform
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ANNA UNIVERSITY: CHENNAI 600 025 MAY 2012DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING PROJECT VIVAVOCE
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WAVELET BASED FEATURE EXTRACTION SCHEME OF ELECTROENCEPHALOGRAPHYPRESENTED BY UNDER THE GUIDANCE OFE.ARUNA-12708106004 MR.C.E.MOHAN KUMAR, M.EM.S.R.PUNEETHA CHOWDARI-12708106043 ASSISTANT PROFESSORB.SASI KALA-12708106050 ECE DEPARTMENTN.SHANTHA PRIYA-12708106052
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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.
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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.
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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.
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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.
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BRAIN COMPUTER INTREFACE WORKING Blocks of Brain-Computer Interface EEG Signal Acquisition Signal Preprocessing Feature Extraction Signal Classification
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LITERATURE REVIEW The history of brain–computer interfaces (BCIs) starts with Hans Bergers 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.
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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.
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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.
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EXISTING SYSTEMFOURIER 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
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EXISITING SYSTEMSHORT 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.
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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
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PROPOSED SYSTEMWAVELET 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.
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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.
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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.
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WAVELET TRANSFORMADVANTAGE 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.
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WAVELET TRANSFORMADVANTAGE 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.
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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
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CONTINUOUS WAVELET TRANSFORMSTEP 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 iswith this section of the signal. The higher C is, the more the similarity.
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CONTINUOUS WAVELET TRANSFORMSTEP 3: Shift the wavelet to the right and repeat steps 1-2 until we‟ve covered the whole signal
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CONTINUOUS WAVELET TRANSFORMSTEP 4: Scale (stretch) the wavelet and repeat steps 1-3
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DISCRETE WAVELET TRANSFORMWavelet 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 awhere a is the dyadic scaling parameter and b is the dyadic shifting parameter
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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.
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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.
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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
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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.
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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.
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SIGNAL DECOMPOSITION The decomposition of the signal leds 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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Thank you!