OAs Removal

8/11/2019 OAs Removal

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Removal of Ocular Artifin EEGAn Improved

Approach Combining D

and ANCPresented by:-

Md. Danish Jamil(2K13/SPD/11)


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Outline

What is EEG?

Types of EEG

Artifacts in EEG

Methodology

RLS Algorithm

Result

Conclusion

References


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What is EEG?

The Electroencephalogram (EEG) is a technique for measurinactivity within the brain.

An EEG signal is a measurement of currents that flow during sexcitations of the dendrites of many pyramidal neurons in thecortex.

The frequency range of EEG is 0-64 Hz.

The amplitude of EEG signal is in microvolts.


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Types wave patterns in EEG


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Artifacts in EEG

Signals that are detected by an EEG but not belong to aorigin are called artifacts.

Types of artifacts:-

Power line artifact

Muscle artifact

Eye blink

Eye movement

ECG artifact


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Ocular artifact

The eye blink and eye movement together are called ocula

The frequency range of ocular artifacts is 0-16 Hz.

The amplitude of ocular artifcats is 5 times of the amplitude o


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Methodology

The recorded EEG signals are contaminated by OAs, this conconsidered to be an additive noise within the EEG signal. So, the following expression:

EEGrec(t) = EEGtrue(t)+kOAs(t)

Where,

EEGrec(t) recorded EEG signal;EEGtrue(t) EEG signal due to cortical activity and without interfer

kOAs(t) OAs due to eye movement.

We can get the EEGtrue(t) from the EEGrec(t) by removing the kefficiently.


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Methodology contd

Our model consists of two main steps. The first is the constructreference signal; the second is the removal the OAs from thesignal by applying ANC, based on a recursive least-squares (


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Reference signal generation

Steps:-

Wavelet decomposition is applied to expand the contaminaget the wavelet coefficients.

According to the minimum risk value, we select the soft thresapply them to the three lowest level coefficients to obtain thcoefficients for those three levels.

Apply wavelet reconstruction to the new wavelet coefficientconstructing the reference signal.


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result


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Conclusion

Our new model is able to provide better attenuation levels fotypes of OAs present in EEG signals.

This performance does produce some processing overhead wthese other methods, but the advantages are sufficient to waalternative system architectures to be considered to mitigateoverheads. The removal of OAs from EEG signals in projects sis just a first step in our work. The usability constraints in portab

environments require single channel processing that is efficieand robust.


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References

[1] T. P. Jung, S. Makeig, C. Humphries, T. W. Lee, M. J. McKeownT. J. Sejnowski, Removing electroencephalographic artifacts byseparation,Psychophysiology, vol. 37, pp. 163178, Sep. 2000.

[2] G Gratton, M. G. Coles, and E. Donchin, A new method for oof ocular artifact,Electroencephalogr. Clin. Neurophysiol., vol. 5468484, Apr. 1983.

[3] J. C. Woestengurg, M. N. Verbaten, and J. L. Slangen, The reeye movement artifact from the EEG by regression analysis in thedomain,BiologicalPsychol., vol.16, pp. 127147, Feb./Mar.1983.

[4] T. D. Lagerlund, F. W. Sharbrough, and N. E. Busacker, Spatiamultichannel electroencephalographic recordings through princcomponent analysis by singular value decomposition,Clin. Neur14, no. 1, pp. 7382, 1997.


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Thank

you

OAs Removal

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