Artifact Detection and Removal from In-Vivo Neural Signals

Artifact Characterization, Detection and Removal for In-Vivo Neural

Recording

Presented By:

Md Kafiul IslamPhD Candidate

Supervisor: Dr. Zhi YangTranslational System and Signal Processing Group

Department of Electrical and Computer EngineeringNational University of Singapore

Presented By Md Kafiul Islam 1

Outline• Introduction

Motivation

Artifact Characterization

Available Methods and their Limitations

• Proposed Algorithm

Stationary Wavelet Transform (SWT) and Filtering

Simulation Results

Comparison with Other Methods

• Conclusion

Applications

Future Work


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 & Spikes along with their

Correlation: more insight on how brain works.

Artifacts

Often corrupt recordings: less constrained environment.

Cause mistakes in interpretation of neural information.

The challenges for in-vivo artifact identification compared

to EEG or other applications:

No prior knowledge about artifacts unlike EEG-artifacts

The broad frequency band of in-vivo data (0.1 Hz – 5 kHz)

Presented By Md Kafiul Islam

Single/Multi Unit Neural Recordings

3

Motivation

Wireless Neural Recording and Signal Processing System


AAnalog

Front Ends

Electrode Array

ADC

On-chip Neural SignalProcessing

TelemetryInterface

(Bidirectional)

· Offset Remove · Power line

Interference Remove· Artifact Remove

Signal Pre-Processing

Neural Signal Processing

(LFP + Spikes)

· Feature Extraction· Classification· Compression

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Artifact Sources and PropertiesInterfering signals that originate from source other than brain of

interest.


Local Field Potential => 0.1 Hz ~ 200 Hz; 0.1 ~ 1 mVpp

Neural Spikes => 300 Hz ~ 5 kHz; 40 ~ 500 µVpp

Artifacts => 0 ~ 10 kHz; 20 mVpp

Sources/Factors:

i) Environmental (e.g. sound/optical interference, EM-coupling, etc.)

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

movement, etc. due to mainly subject motion )

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

Fig. Wireless In-Vivo Recording of Neural Activity1

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Artifact Appearance• 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/twice in the whole recording sequence

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


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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Artifact Types and Spectral Characteristics


Template Extract

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Available Methods and Limitations*EEG or Other Physiological Signal Recordings

Independent Component Analysis (ICA)

or Canonical Correlation Analysis (CCA)

Offline and manual intervention; at best semi-automatic

Suitable for global artifacts only

Assumption of Independence/Un-Correlation

Adaptive filtering

Reference channel to record artifact source

Wavelet-enhanced ICA/CCA (wICA/wCCA)

Identification of artifactual IC is difficult

DWT involvement may produce severe distortions

Empirical Mode Decomposition (EMD or EEMD)

Computational complexity and storage problem

*No literature particularly on artifacts for in-vivo neural signals


Proposed Solution


Stationary Wavelet Transform (SWT): To separate possible artifactual events.

Filtering: To finally detect artifacts from signals of interest.

(Two frequency bands where signal power is relatively low=> BPF@150 - 400 Hz , HPF @ 5 kHz)

Threshold: Proposed a modified universal threshold that depends on signal histogram.

Inverse Stationary Wavelet Transform (ISWT): To reconstruct artifact-free signals.

ArtifactualData

Artifact-free Data

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Why SWT ?SWT :

Usually DWT or SWT is preferred over CWTwhen signal synthesis is required

CWT is very slow and generates way toomuch of data.

SWT is translation invariant where DWT isnot. So better reconstruction result. (No lossof information, preserves spike data and doesn’tgenerate any spike-like artifacts)

Computational complexity is between DWTand CWT.

DWT:[O(N)] SWT[O(NL)] CWT [O(N L log2N)]

N = length of signal, L = decomposition level


Wavelet Transform in General: Good time-frequency resolution Non-linear, non-stationary signals (e.g. neural signals) Both single and multi-channel recordings Both detection (from decomposed coefficient) and removal (thresholdingand reconstruction by inverse transform) of artifacts.

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Digital implementation of SWT: A 3 level SWT filter bank and SWT filters A 2-Level DWT decomposition and the

reconstruction structures

Why SWT (2)… ?


FPR

TP = # True Positives (Hit)FP = # False Positives (False Alarm)TN = # True Negatives (Correct Rejection)FN = # False Negatives (Misdetection)

0 100 200 300 400 500 600 700 800 900 1000-10

-5

0

5

Spike data comparison after artifact removal

Norm

alize

d A

mplitu

de

0 500 1000 1500 2000-15

-10

-5

0

5

10

15

Time Sample

Ref

DWT

CWT

SWT

Original Spike

(True Positive)

False Spike

(False Positive)

False Spike

(False Positive)

Original Spike

(True Positive)

Original Spike

(True Positive)

Effect of Filtering

– Separate spikes from high frequency artifacts (e.g. type-3)


0 1 2 3 4 5 6 7 8

-1000

-500

0

500

0 1 2 3 4 5 6 7 8

-1000

-500

0

500

Am

pli

tud

e

0 1 2 3 4 5 6 7 8

-1000

-500

0

500

Time, Sec

Raw Data

Reconstructed by only SWT

Reconstructed by SWT + Filtering

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ROC for Spike Detection

False Positive Rate (FPR)

Tru

e P

osit

ive

Rat

e (T

PR)

SWT Only

SWT + Filtering

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Threshold Value

• Universal Threshold:

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

• Modified Threshold:


k = kA for approx. coef. kD for detail coef.

By empirical observation from signal histogram

5 < m < infinite

2 < n < 3

D4, D5, D6 contain the frequency band of spikes.

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 sensitive to input change, lower bias and more importantly continuous.


Hard GarroteSoft

Data Synthesis for Simulation


Clean in-vivo Data (Reference)

Raw In-Vivo DataWith Artifacts

Extract Artifact Templates

Synthesized Artifactual Data

Random Amplitude Random

Location

Random Duration

Performance Evaluation (Important Definitions)

Simulation is performed on both real andsynthesized (semi-simulated) signal databasefrom different subjects.

Removal Measurement

Lamda, λ: Amount of artifact reduction

ΔSNR: 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 and neural signal as noise:

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Removal Results (Tested on Synthesized Sequence)



Removal Results (Tested on Real Sequence- Rat Data)


Removal Results (Tested on Real Sequence-Monkey Data)

Results (Tested on Synthesized Sequence)


Quantitative Evaluation


Comparison with Other Methods


dB

dB

Artifact Artifact

Artifact Artifact

dB

dB

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Applications

• Any open/closed loop neural system (e.g. BCI,

neural prostheses, basic neuroscience/clinical research)

• Removal of stimulation artifacts.

• Both online and offline implementation

• Both single and multi-channel recordings


Future Work

– Algorithm optimization.

– More simulations to fine tune the algorithm.

– Hardware implementation.

– Publish the artifact database to public domain.

– Development of a Software (MATLAB based)

tool: Free licence


The End

Q & A

Thank You
