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.

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.

5. KEY WORDS
 Electroencephalogram (EEG)
 Brain-Computer interface (BCI)
 Wavelet Transform (WT)
 Continuous Wavelet Transform (CWT)
 Discrete Wavelet Transform (DWT)
 Visually Evoked Potential (VEP)
 Discrete Fourier Transform (DFT)

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.

22. CONTINUOUS WAVELET TRANSFORM
STEP 3: Shift the wavelet to the right and repeat steps 1-2 until we‟ve
covered the whole signal

23. CONTINUOUS WAVELET TRANSFORM
STEP 4: Scale (stretch) the wavelet and repeat steps 1-3

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.

36. RESULTS AND OUTPUTS
 Outputs.docx

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.

42. THANK YOU