artifact removal for in-vivo neural signal

8/11/2019 Artifact Removal for in-vivo Neural Signal

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Artifact Detection and Removal

for In-Vivo Neural SignalPresented By:

Md Kafiul IslamA0080155M

Supervisor: Dr. Zhi Yang

Department of Electrical and Computer Engineering

National University of Singapore

20thFeb, 2013

Presented By Md Kafiul Islam


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Outline

IntroductionMotivation

Artifact Characterizing: Its Types, Sources and Properties;

Dynamic range analysis

Literature Review

Proposed Method

Wavelet Transform Based Artifact Removal

Simulation Results

Comparison with Other Methods

Future Work

Real-time Implementation of Proposed Algorithm



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Motivation

Closed Loop Neural System for BCI or neural prosthetic Application



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Motivation


Typical In-Vivo Neural Signal Processing Steps

Remove50/60HzNoise

LPF @200Hz

OffsetRemove

Smoothing AlignmentUnder

SamplingLFP

Data

Notch FilterField Potentials Recording

Artifact Detect &Remove

HPF @1Hz

BPF(300Hz~5kHz)

SmoothingNormalization& Alignment

SpikeData

Spike Detection

Spike Detection & Recording

FeatureExtraction

Classification

Compression


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MotivationIn-vivo neural recording

Investigate brain information processing & data storage

Better Spatio-temporal resolution.

Better Signal-to-Noise Ratio(SNR).

The study of both LFP& Spikesalong with their correlation: more insight on

how brain works.

Artifacts

Recordings corrupted by artifacts, especially in less constrained

environment.

Cause mistakesin interpretationof neural information.

Signal Preprocessing: Automatic Detection and Removal of Artifacts

The challenges for in-vivo artifact identification compare to EEG/MEG-

artifacts are:

No prior knowledge about artifacts unlike EEG-artifacts

The broad frequency band of in-vivo data (0.1 Hz 5 kHz) makes it difficult

to separate artifacts from signal


Single-multi unit


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What is Artifact!?

In neural recording, artifacts are interfering signals that originatefrom some source other than the brain of interest.

In Vivo Recording of Spontaneous

Neural Activity of a freely moving rat

Offending artifacts may obscure, distort, or completely

misrepresent the true underlying recorded signal being observed.



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What is Artifact (Cont)

Local :localized in space, i.e. appear only in a single recording channel.

Global :across all the channels of an electrode at the same temporal window. Irregular:only once in the whole recording sequence

Periodic:regular manner possibly due to some periodic motions of the subject.


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SignalAmplitude,mV

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Time, Sec

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2

ch 1 ch 2

ch 4

ch 6

ch 3

ch 5

ch 7 ch 8

Global Artifacts

Irregular/Local Artifacts Periodic Artifacts

Perspective Artifact Category/Class

Repeatability Irregular/No Periodic/Regular/Yes

Origin Internal External

Appearance Local Global


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Possible Artifact Sources

Artifacts may generate from 3 general factors :

i) Environmental factors (e.g. power noise, sound/optical interference, EM-coupling

from earth: 7.82 Hz and harmonics*, etc.)

ii) Experiment factors (e.g. electrode position altering, connecting wire

movement, etc. due to mainly subject motion )

iii) Physiological factors (e.g. EOG, ECG, EMG, BCG,etc.)



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Artifact Types


(Identified by Empirical Observations Based on Real Neural Sequence, there could be

many other types as well)


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Properties of Artifacts

Usually the artifacts have very large

magnitude compared to the neural data

of interest, i.e. spike and local field

potential.

The frequency range for artifact may

vary from very low(motion artifact) to

high frequency(artifact due to residue

charge) range.


Local Field Potential => 0.1 Hz ~ 200 Hz, 0.1 ~ 1 mVppNeural Spikes => 300 Hz ~ 5 kHz, 40 ~ 500 uVppArtifacts => 0 ~ 10 kHz or even higher, max amplitude as high as

20 mVpp. (From real data observation)


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Properties of Artifacts(Comparison in Spectral Domain with Neural Signal of Interest)



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Problems with Artifacts

Can cause electronics saturation [1]

High dynamic range required (Higher ENOB in ADC) [2]

Mislead to spike detection (high freq) [3]

Misinterpretation for LFP recording(low freq) [4]


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Time, Second

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olt

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Time, Second

Voltage,

Vo

lt

Afte r BPF of In Vivo data fro m 300 Hz t o 5 kHz

False Spike detection

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Time Sample

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Time, Second

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Local Field Potential

[1]

[2] [3][4]


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Dynamic Range Study


Subject

(Fs in kHz)

B.W.

No of Data

Sequences

(Data Length

in min)

Amplifier Circuit

Noise Floor

(Vrms)

DR without

Artifact

(Mean SD)

(Full Spectrum Datain dB)

DR with

Artifact

(Mean SD)

(Full Spectrum Data

in dB)

Increase in DR

(Full Spectrum

Data in dB)

DR without

Artifact

(Mean SD)

(Spike Data indB)

DR with

Artifact

(Mean SD)

(Spike Data in

dB)

Increase in

DR

(Spike Data

in dB)

Rat

Hippocampus

(40)

0.1 Hz 10 kHz

134

(15) 1 69.01 2.10 82.44 4.21 13.43 59.21 4.32 78.35 8.26 19.14

Human Epilepsy

(32.5)

0.5 Hz 9 kHz

64

(18) 1 34.45 3.42 64.36 3.42 29.90 28.82 4.605 55.75 6.94 26.92

0 5 10 15

40

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50

55

60

65

70

75

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Artifact Amplitude, mV

DynamicRange,

dB

Full Spectrum Data with T2 art

Spike Data with T2 art





Full Spectrum DRWithout Artifact

Spike DRWithout Artifact


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LiteratureReview(No literature particularly on artifacts for in-vivo neural signals)

EEG artifacts removal:

ICA, CCA (offline and manual intervention, at best semi-automatic,

works for global artifacts only)

Adaptive filtering (Reference channel to record artifact)

Wavelet-enhanced ICA/CCA (Identification of artifactual

Component is a tough job, DWT involved)

HHT, e.g. EMD or EEMD (Computational complexity and storage

problem)



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LiteratureReview Limitations of Current Methods

Assumes the sources are independentand at most onesource can be Gaussiandistributed (ICA)

Assumes the sources are maximally un-correlated(CCA)

Requires extrareferencechannel to record artifacts(Adaptive filter)

Assumes signals and artifacts to be stationarylinear

random process and known spectral char(Wiener filter)

Filter model to be linear, a-prioriestimation is Gaussian

and work only for unimodaldistribution (Kalman filter)



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Comparison of Current Artifact Removal Techniques

for Physiological Signals(EEG, EMG, ECG, ECoG, fNIRS, PPG, Respiration, etc.)


Adopted from: Kevin T. Sweeney, Tomas E. Ward, and Sean F. McLoone, Artifact Removal in Physiological SignalsPractices and

Possibilities, IEEE Transactions on Information Technology In Biomedicine, vol. 16, no. 3, May 2012.


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Comparison of Current Artifact Removal Techniques

(Computational Time)

Adopted from: Kevin T. Sweeney, Sean F. McLoone, and Tomas E. Ward The use of Ensemble Empirical Mode Decomposition

with Canonical Correlation Analysis as a Novel Artifact Removal Technique, IEEE Transactions on Biomed Eng., 2012.



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About Wavelet Transform(A Multi-resolution Analysis)

Split Up the Signal into a Bunch of Signals

Representing the Same Signal, but all Corresponding to Different Frequency Bands

Only Providing What Frequency Bands Exists at What Time Intervals


( ) ( ) ( ) dts

ttx

sss

xx

== *1

,,CWT

Translation

(The location of

the window)

ScaleMother Wavelet

From http://www.cerm.unifi.it/EUcourse2001/Gunther_lecturenotes.pdf, p.10

Wavelet

Small wave

Means the window function is of finite length

Mother Wavelet

A prototype for generating the other window functions

All the used windows are its dilated or compressed and shifted

versions

Scale S>1: dilate the signal

S


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Why Wavelet Transform:

Artifacts:

Appear as abrupt change in signal amplitude (e.g. motion artifacts)

Overlaps with neural signal in both temporal & spectral domain

Different waveform shapes


WT:

Good time-frequency resolution

Can work with non-stationary signals, e.g. neural signal

Easy to implement [complexity:DWT-> O(N); FFT -> O(N log2N);N-> length of signal]

Can work for both single and multi-channel recordings

Most importantly it can be used for both detection(from decomposed coefficient) and

removal(thresholding and reconstruction) of artifacts.


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Why Wavelet Transform:

DWT is applied on raw neural signal (decomposition level, L = 5) which is contaminated by artifacts. The coefficients of decomposed signal components can

localize the artifact regions.



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Proposed Solution


Purpose of Algorithm

Minimum (or almost no)

distortionto neural signal

Remove artifacts as much as

possible

Should be automatic

Robustnessis important

Able to implement online

Should work in both single and

multi-channel analysis

Should not depend on artifact

types.

Traditional Wavelet

Denoising


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Why SWT (1) ?We prefer to use Stationary Wavelet Transform (SWT) instead of DWTand CWT:

Usually DWT or SWT is preferred over CWT when signal synthesis is required

CWT is very slow and generates way too much of data.

SWT is translation invariant where DWT is not. So better reconstruction result (No loss ofinformation, preserves spike data and doesnt generate any spike-like artifacts).

Choice of mother wavelets for CWT is limited.

SWT implementation complexity [O(N L)] is in between DWT [O(N)] and CWT [O(N Llog2N)].

N= length of signal, L = decomposition level


Digital implementation of SWT:

A 3 level SWT filter bank and SWT filters

A 2-Level DWT decomposition and the

reconstruction structures


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Why SWT (2) ?


FPR

TP = # True Positives (Hit)

FP = # False Positives (False Alarm)

TN = # True Negatives (Correct Rejection)

FN = # False Negatives (Misdetection)

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NormalizedAmplitude

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Time Sample

Ref

DWT

CWT

SWT

Original Spike(True Positive)

False Spike(False Positive)

False Spike(False Positive)




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Effect of Filtering

Separate spikes from artifacts


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500Real Data from Monkey Front Cortex

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Amplitude

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Time, Sec

Original

Reconstructedby only SWT

Reconstructed bySWT + Filtering

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ROC for Spike Detection

FPR

TPR

SWT + Filtering

Only


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ThresholdValue

Universal Threshold:

Wi= Wavelet coefficients; i = variance of Wi; N = length of signal

Modified Threshold:


k= kAfor approx. coef.

kDfor detail coef.

By empirical observation from

signal histogram

5 < m < infinite

2 < n < 3

D4, D5, D6contain the frequency

band of spikes.


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Choice of Threshold Function (Garrote) Hard: Discontinuous which may produce large variance (very sensitive to small changes

in the input data)

Soft: Continuous but has larger bias in the estimated signal (results in larger errors)

Garrote: Less sensitiveto input change, lower bias and more importantly continuous.


Hard GarroteSoft

P f E l ti


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Performance Evaluation(Important Definitions)

Simulation is performed on both real andsynthesized (semi-simulated) signal database

from different subjects.

Removal Measurement

Lamda, : Amount of artifact reduction

SNR: Improvement in signal to noise (artifact) ratio

Distortion Measurement

RMSE: Root mean square error

Spectral Distortion:


x(n) = Reference signal

x(n) = Reconstructed signal

y(n) = Artifactual signal

e1(n) = error between x & y

e2(n) = error between x & x

Rref= auto-correlation of reference signal

Rrec= cross-correlation between

reference and reconstructed signal

Rart= cross-correlation between

reference and artifactual signal

Artifact SNR:

Consider artifact as signal andneural signal as noise:

h i f i l i


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Data Synthesis for Simulation


Clean in-vivo

Data (Reference)

Raw In-Vivo Data

With Artifacts

Extract Artifact

Templates

Synthesized

ArtifactualData

Random

AmplitudeRandom

Location

Random

Duration


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Results (Tested on SynthesizedSequence)



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Results (Tested on SynthesizedSequence)



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Results

(Tested on RealSequence-1)


Data Sample 1: Rat Hippocampus


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Results



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4Recorded vs Reconstructed (Before & After Artifact Removal)

Time Sample

SignalAmplitude,mV

Reconstructed

Recorded

Data Sample 1: Rat Hippocampus


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Results(Tested on RealSequence-3)

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Time Sample

SignalAmplitude

Reconstructed

Original


Data Sample 3: Cat Spinal Cord (High-pass Filtered @300 Hz)

5.7 5.75 5.8 5.85 5.9 5.95 6 6.05 6.1 6.15

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Spike Data before & after artifact removal

Time Sample

NormalizedAmplitude

Original Spikes

Reconstructed Spikes


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Results


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x 105

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-600

-400

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400Original vs Reconstructed

Time Sample

SignalAmplitude

Reconstructed

Original


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Spike Data before & after artifact removal

Time Sample

NormalizedAmplitude

Original Spike Data

Recons Spike Data

Data Sample 4: Monkey Front Cortex


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Quantitative Evaluation-1



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Quantitative Evaluation-2


i i h h h d


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, dB

dB

dB

Artifact

Artifact

C i i h O h M h d


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Artifact

dB

Artifact

dB

C i ith Oth M th d


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Date RMS

Date RMS


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Conclusion

First time(to best of knowledge) Investigation of

artifacts for in-vivo neural data Artifact characterization

Dynamic range study due to artifacts

Database synthesis for quantitative evaluation Proposal of a detection and removal algorithm

Threshold improvement

Robust (Depends only on neural signals spectral char)Automatic

Real-time implementable



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Applications

Any closed loop neural system (e.g. BCI orneural prostheses)

Basic neuroscience study

Clinical research Removal of stimulationartifacts.

Both online and offline implementation

Both single and multi-channel recordings



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Future Plan-1

Optimize the algorithm further to allow faster

processing and less storage.

Perform additional experiments (simulations) in

order to fine tune the algorithm.

Proceed to hardware implementation and

perform real-time experiments to verify the

actual performance in practice.



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Future Plan-2

Publish the artifact database to public domain to

facilitate future research.

Development of a Software (MATLAB based) tool

for offline analysis that will be open for all to

download.


Future Plan 3


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Future Plan-3

Primary Work (with Dr. Amir)

A Wavelet Transform Based Algorithm for UsableSpeech*

Segments Extraction from Co-Channel Signals


Unvoiced frame and

its DWTVoiced frame and its

DWT

*Can be replaced by in-vivoneural signal

P bli ti


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Publications

In Preparation (Journal):

1. Md Kafiul Islam, Amir Rastegarnia, Nguyen A. Tuan, and Zhi Yang, A Wavelet Based Artifact Detection

and Removal Algorithm for In-Vivo Neural Recording In preparation for submission to Journal of

Neural Eng.

2. Jian Xu, Md Kafiul Islamand Zhi Yang, A 13W14-BitModulator for Wide Dynamic Range Neural

Recording In preparation for submission to IEEE Trans. On BioMed. Circuit & Systems.

Submitted (Conference):

1. Jian Xu, Md. Kafiul Islam, and Zhi Yang, A 13W 87dB Dynamic Range Implantable Modulator for

Full-Spectrum Neural Recording Submitted to EMBC13

2. Azam Khalili, Amir Rastegarnia, Md Kafiul Islam, Zhi Yang, A Bio-Inspired Cooperative Algorithm for

Distributed Source Localization with Mobile Nodes Submitted to EMBC13

Accepted (Conference):

1. Md. Kafiul Islam, N Tuan, Y. Zhou, and Z. Yang, Analysis and Processing of In-Vivo Neural Signal for

Artifact Detection and Removal - Accepted in the International Conference on BioMedical Engineering

and Informatics (BMEI), October 2012, Chongqing, China.



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