Rambabu Paper

7/31/2019 Rambabu Paper

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RECONFIGURABLE FILTER BASED SELF PACED

ARTIFACTS REMOVAL SCHEME FOR

NEUROLOGICALLY EXTRACTED FEATURES

Mr. Chunchu Rambabu

Department of Instrumentation, Research Scholor,

Rayalaseema University, Kurnool, India.

E-mail:[email protected],Mob: +91-9840-639-203.

Dr. B Rama MurthyFaculty of Instrumentation, S.K. University, Anantapur.

E-mail:[email protected],

Mob : +91-944-0780963.

Abstract

Visual Evoked Potentials originates from neurons in the cortex, outer layer of brain andare oscillating and event-related potentials in nature. These signals are responses to visual

stimulants and extensively used in neuropsychological studies. With electroencephalogram as

background, these signals have poor signal-to-noise ratio which is a major hurdle in its analysis.Averaging is the primitive scheme to reduce the EEG effects on VEP signals. In this work, a

method to reduce electroencephalogram (EEG) artifacts from Visual-Evoked Potentials (VEP) in

brain-computer interface (BCIs) design is presented. For the test composite signal, the frequency

ranges corresponding to stimulus-related VEP components were located using statistical

coefficient selection (SCS). The resulting cyclic frequency spectrum provides VEP frequencyband detection. Using this identified frequency ranges, evolved adaptive filters are employed for

EEG artifacts reduction. Adaptive Noise Cancellation (ANC) scheme and Wavelet DenoisingAlgorithm (WDA) are used to distinguish VEP components and EEG artifacts. In this work, a

reconfigurable adaptive noise cancellation filter that constitutes the backbone of a Adaptive

Noise Cancellation (ANC) scheme is designed and applied to remove the EEG artifact effects

that occur along with VEP signals.

Key words: VEP signals, wavelet denoising algorithm (WDA), Adaptive noise cancellation

(ANC), EEG Artifacts, Reconfigurable adaptive noise cancellation filter.

1. IntroductionThe proposed method shall compare the results amongst various artifact removal techniques.The advantage of SCS is that it facilitates statistical analysis of the signal throughout the time

additionally; the results shall focus on recovering the general shape of VEP effectively. Usingthe Wavelet Denoising algorithm, the selection procedure is to be made automatic and this

eliminates setting threshold manually.

Conventionally, a DWT based approach does exist, but, the shift-variance property ofDWT is considered as a major disadvantage in solving pattern matching problems. Alternatively,
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the proposed stationary Wavelet Transform is an upgraded tool to counteract this demerit (shift-

invariant version of DWT). In SWT upsampling is used in place of down sampling. However thenumber of samples remains the same.

In Bio-signal processing there is a need to

(i) Reduce EEG artifacts from VEP and use the processed signal in BCI design.(ii)

Acquire test composite signals with frequency ranges corresponding to stimulus relatedVEP components.

(iii) Perform VEP frequency band detection.

(iv)Identify frequency ranges and accordingly use filtering any suitable techniques to reduceEEG artifacts in these frequency bands.

The above factors are effectively studied in this research.

2. Reported WorkThe most practical and widely applicable BCI solutions are those based on noninvasive

electroencephalogram (EEG) measurements recorded from the scalp. In [9] the effectiveness ofSSVEP-based BCI designs is due to several factors. In this paper, the authors address a novel

application of the SSVEP-based BCI design within a real-time gaming framework. Furthermore,[9] reported that to elicit an SSVEP signal at a certain frequency, a flicker stimulus must be

modulated at that frequency, while a checkerboard pattern need only be modulated at half thatfrequency, as the SSVEP is produced at its rate of phase-reversal or alternation rate. The two

feature extraction methods can be directly compared for the offline data, given that the methods

are used to classify the same data set. [9] has presented the application of an effective EEGbasedbrain-computer interface design for binary control in a visually elaborate immersive applications.

[11] propose a new multi-stage procedure for a real time brain machine/computer

interface (BCI). The developed system allows a BCI user to navigate a small car (or any other

object) on the computer screen in real time, in any of the four directions and to stop it ifnecessary.[11] says that a brain-computer interface (BCI), or a brain-machine interface (BMI), is

a system that acquires and analyzes brain signals to create a high-bandwidth communicationchannel in real-time between the human brain and the computer or machine. They present a newBCI system with a visual stimulation unit designed as a smart multiple choice table in the form

of an array of four small checkerboard images flickering with different frequencies and moving

along with the controlled object. They [11] demonstrated the application of a fast online blindsignal separation (BSS) algorithm for automatic rejection of artifacts and noise reduction, a bank

of bandpass filters with non-stationary smoothing and an adaptive fuzzy classifier.

BCI is a communication system that recognizes human commands from brainwaves only

and reacts to them. Several BCIs based on EEG measurements are presented below. [12] Saysthat BBCI (Berlin Brain Computer Interface) was presented by the research team from

Fraunhofer FIRST institute at the Cubit 2006 exhibition in Hanover. With this BCI a disabled

person can write words and control the mouse pointer by means of his brain waves. [12]Mentioned the visual evoked potentials are significant voltage fluctuations resulting from

visually evoked neural activity. Neurons in the visual cortex response to the flickering stimuli at

the frequency of the flickering light. [12] concluded that the classification of EEG signals, which

are recorded when person was stimulated with two different stimuli, can be performed by usingthe artificial neural networks

[13] Introduces a system that can help the disabled persons, who have no motor control

left for communication besides eye movements, to achieve an acceptable level of


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communication. The system incorporates a brain-computer interface (BCI) for connecting the

brain to the computer. Several issues are crucial to further development and expanded utilizationof the BCI technology. The first issue is the information transfer rate. [13] Concluded a higher

performance can be expected when using more visual stimuli and more sophisticated signal

processing methods, optimized for each user individually.

In [14] a high-speed size and orientation invariant eye-tracking method, which canacquire numerical parameters to represent the size and orientation of the eye, is presented. [14]In

order to overcome these problems, template matching is used with genetic algorithm . A large

number of researches have been carried out by [14] in eye-gaze direction and eye-tracking. Thecontrast color is used as a feature of the iris, therefore, the input data is gray scale values.

Moreover, considering general use of this system, a general template should be used [14]. [14]

Says that after an initial population is generated with random numbers, GA is started. Thematching process is executed between the template image and a target frame in a fitness

function. In [14] a high-speed and orientation invariant eye-detection and tracking method,

which can acquire numerical parameters to represent the size and orientation of the eye. In this

paper, it was concluded that high tolerance for human head movement and real-time processing

is needed for many applications such as eye-gaze tracking.[17] Tell us that the energies of various frequency brands decomposed by a wavelet

packet transformed were used as features in detecting different movement patterns in self-pacedBCI system. These features were linearly combined to generate single features, with coefficients

of the linear mapping determined by a genetic algorithm (GA).

[21] An SWT resolves the shift-variancy problem associated with the DWT byeliminating the downsampling operator from the multi-resolution analysis. For all neurological

phenomena, the features were calculated for the lowest approximation and detail levels. Versions

of the wavelet function will match the high-frequency components of the single and the dilated

versions will match the low-frequency oscillations.

3. EEG Electrodes

Type Description

Beta The rate of frequency change lies between 13 and 30 Hz, and usually has a low voltage (between 5-

30 V).Beta is the brain wave usually associated with active thinking, active attention, and focus onthe outside world or solving concrete problems. It can reach frequencies near 50 Hertz during

intense mental activity.

Alpha The rate of change lies between 8 and 13 Hz, with 30-50 V amplitude. Alpha waves have beenthought to indicate both a relaxed awareness and also inattention. Alpha waves can be reduced or

eliminated by opening the eyes, by hearing unfamiliar sounds, or by anxiety or mental

concentration.

Theta Theta waves lie within the range of 4 to 7 Hz, with an amplitude usually greater than 20 V. Theta

arises from emotional stress, especially frustration or disappointment. Theta has been alsoassociated with access to unconscious material, creative inspiration and deep meditation. The large

dominant peak of the theta waves is around 7 Hz.

Delta Delta waves lie within the range of 0.5 to 4 Hz, with variable amplitude. Delta waves are primarily

associated with deep sleep, and in the waking state, were thought to 15 indicate physical defects in

the brain.

Gama Gama waves lie within the range of 35Hz and up. It is thought that this band reflects the mechanism

of consciousness - the binding together of distinct modular brain functions into coherent percepts

capable of behaving in a re-entrant fashion.


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4. MethodologyThe proposed scheme to reduce background EEG artifacts from VEP signal involves the

following stages.

1. P300 frequency band detection from VEP signalsVEP Signal band detection:

Using the cyclostationarity property, a novel model for enhanced detection ofVEP is presented. The advantage of this technique is trials are not required to be phase

locked. Concatenation of trials which is based on internal similarities, introducescyclostationarity. The periodic repetition of P300 components in VEP trials enables

cyclic analysis of the VEP signals. Similar to real-signal recordings, VEP and EEG

signals are emulated to study cyclostationary property.2. Bandpass filtering

3. Wavelet Transforms based Statistical Coefficient Selection method and denoising

algorithm. This involves Signal analysis using Cyclo model.

4.1 Wavelet Analysis

Shift invariant Wavelet Transform (SWT) has been applied extensively by using wave-likesignals referred as wavelets. These wavelets decompose and extract information from thetime-varying non-stationary signals such as neuro-electric waveforms. Comparing with Fouriertransform (FT), SWT provides joint-time frequency resolution for details extraction. This work

proposes automated procedure, where the signal is to be decomposed into appropriate number of

scales and each scale is to be reconstructed into a time series.

4.1.1 Stability of wavelet filter

The filter transfer function () ()

() () ()() with the following properties

(i) Po(z)=zNPo(z),(ii) P1(z)=czNP1(z) for c=1, and(iii) H o(z)Ho(z)+H 1(z)H1(z)=1.Substituting Ho(z) and H1(z) into property (iii)Po(z)Po(z)+P1(z)= D (z)D(z)

Using property (i) and (ii), we get () () ()()Since c=1, c=ej. Thus,

[Po(z)+jej/2 P1(z)][Po(z)-je

j/2 P1(z)]=z-ND (z)D(z). (1)

SinceHo(z) andH1(z) are stable,D(z) has all zeros inside the unit circle and z-ND (z) has all zeros

outside the unit circle. Therefore, the RHS (hence the LHS) of (1) does not have zeros on unitcircle. Suppose Po (z)+je

j/2 P1(z)has n1 zeros inside the unit circle, and n0 =N-n zeros outside

the unit circle. Then

Po(z)+jej/2

P1(z)=D1(z)z-noD o(z) . (2)

Where Do(z)=1+no

n=1 do,nz

-n

and D1(z)= 1+n1

n=1 d1,nz

-n

.D1(z) contains n1 zeros inside the unitcircle and z-no D o(z) contains all the no zeros outside the unit circle. Then ..... (2)Po(z)-je-j/2 P1(z)=*D 1(z)znoDo(z)z

NPo(z)-je

-j/2cz

NP1(z)=*D 1(z)znoDo(z) .... (3)

Po(z)-jej/2 P1(z)= *D 1(z)z -n1Do(z)

From (1), (2) and (3), it be conclude

Z-N D () D() =2 Z-N D 1() D 0(Z) D1(Z) D0(Z)


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D(z) has N zeros inside the unit circle, and D0(z), D1(z) have no,n1 zeros inside the unit circle

respectively. Therefore, we know that D(z)= D0(z) D1(z) and=1.simpliflying (2) and (3)

()

()()

()

()

() ()

() () ()

and ()

() ()

()

() ()()

()() ()

()Let() ()() and()

()()

It is clear thatAo(z) andA1 are unit magnitude, all pass, and stable. Then we can obtain

() ( () ())

() () ()

Where d = -je-j/2, so d= 1

5. Design of Adaptive Noise Cancellation Filter1. Extract mean value of the mixture.2. Remove the mean.3. Obtain average of the covariance matrix of available vectors using covX=X*X'/104. Obtain diagonal matrix DV of eigenvalues and a full matrix DV whose columns are the

corresponding eigenvectors such that covX*EV=EV*DV.5. Sort eigenvalues in ascending order and extract diagonal elements of the matrix

[YY,I]=sort(diag(DV))

6. Arrange eigen vectors matrix in accord to indexed matrix form7. Get eigenvalues of first N rows and columns (for N independent signal components).8. Get eigenvector of first N rows and columns -as we have N independent signal

components

9. Calculate the Whitening matrix V=(DR^-0.5)*ER'10. Calculate the whitened input vector v=V*X

11. Scale the whitened input vector by dividing by number of samples.12. Calculate the correlation coefficients between the N separated signals and the N original

signals

13. Compute the mean square error.

x(n)=s(n)+w(n)

A

(n)X(n-1)

B

X(n-2)

C (n)X(n-3) D

Wavelet filter

Note: down sampler & z-1 delay by unit sample

Noise

elimination

filter

4

4

4

4

Z-1

Z-1

Z-1


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A (n)

B

C (n)D

Fig 1: Noise elimination filter

Where A = () B = () C = () D = ()

6. Results

In this section, the efficiency of the proposed algorithm to separate the signal and noise is studiedfor two different cases corresponding to low frequency and high frequencies. For each case, the

correlation of the original signal with the separated signals and the extracted eigenvectors and thefinal trained weights are tabulated.

The original signal and noise (Gaussian) is shown in figure 2a and 2b. the signal and

noise are mixed and also in the same frequency, so that normal filtering frequency techniquescheme cannot be used. The observable mixtures are shown in figure 3. The MSE i.e. different

between separated and original signal is tabulated in the table 1.

Case (i): Low frequency signal and artifacts (transition below steady state)

Time samplesFig 2a: Original noise (O1)

Whitening Separation

Estimation

Am

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Time samples

Time samplesFig 2b: Original signal (O2)

Time samplesFig 3: Mixtures

A

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A

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Time samples

Fig 3a: Separated signal (S1)

Time samplesFig 3b: Separated noise (S2)

Table 1: Separated and original signal correlation values

Correlation with separated Meansquareerror

Trained weights DR ER

auto

correlation(S1)

Cross

correlation(S2)

[ ]-0.7114 0.4300

-0.0945 -0.33060.4852 -0.2573-0.4995 -0.7998O1

original0.0138 -0.9994 0.0161 [ ]

O2original

0.9986 0.0154

Case (ii): High frequency signal and artifacts (Both above and below steady state transition)

Time samples

A

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Fig 4a: Original signal (O1)

Time samplesFig 4b: Original noise (O2)

Time samplesFig 5: Mixture signals

A

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Time samplesFig 5a: separated noise (S1)

Time samplesFig 5b: Separated signal (S2)

Table 2: Table 1: Separated and original signal correlation values

Correlation with separated Meansquareerror

Trained weights DR ER

autocorrelation

(S1)

Crosscorrelatio

n(S2)

[ ]0.5880 - 0.5875

0.1706 0.2984 -

0.4105 0.3650

0.6757 0.6577O1original

0.1852 -0.9810 3.9496 [

]O2original

0.9840 0.1756

6. ConclusionIn this work, VEP signals were collected using different electrodes placed at proper locations in

the Human Brain. The selection of frequency range was based upon SCS technique. Wavelet

Denoising Algorithm (WDA) was used to distinguish VEP components and EEG artifacts. Once

the VEP signals was separated, a adaptive noise cancellation filter scheme was designed andapplied to remove the EEG artifact effects in the VEP signals.

7. Future WorkFuture work shall focus on design of such filters on dedicated reconfigurable hardware.

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Rambabu Paper