PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS"

PhD Oral Defense

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Presented By: Md Kafiul Islam

(A0080155M)

Supervisor: Dr. Zhi Yang

Department of Electrical and Computer Engineering National University of Singapore

28th Oct, 2015

Outline

• Background • Problems and Motivation • Thesis Objectives • Literature Review • Presentation of Thesis Contributions

– Artifact Study on in-vivo neural data – Proposed Artifact Removal Algorithms

• In-Vivo Neural Signals • EEG for Seizure Detection and BCI

• Summary Contributions • Future Work

Presented By Md Kafiul Islam ([email protected])

2

Background-1: In-Vivo Neural Signals


Extra-cellular In-Vivo Neural Recordings

Invasive brain recording technique

To Investigate brain information processing & data

storage

Better Spatio-temporal resolution and SNR than non-

invasive brain recordings.

Study of both LFP & Spikes along with their

correlation: more insight on how brain works.

• Local Field Potentials (LFP) (0.1-200 Hz)

– Population activity from many neurons

• Neural Action potentials /Spikes (300-5000 Hz)

– Activity of individual Neurons

1.083 1.0835 1.084 1.0845

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Spikes

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Single-multi unit


Gamma

EEG is the recording of the brain's spontaneous electrical activity over a period of time by placing flat metal discs (electrodes) attached to the scalp.

• EEG Rhythms

• Transients

Background-2: EEG and its Characteristics

Scalp EEG is Most popular and widely used brain recording technique

1) Low-cost 2) Non-invasive 3) Easy to use 4) fine temporal resolution

Typical Scalp EEG B.W.: 0.05Hz – 128 Hz

4

Motivation-1

Artifacts are unwanted signals originated from non-neural

source

Recordings corrupted by artifacts, especially in less constrained

environment.

Cause mistakes in interpretation of neural information.

Artifacts need to be identified and removed for reliable data

analysis.

The challenges for in-vivo artifact identification compare to EEG

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

Existing artifact removal methods are intended for EEG, So can’t be

applied directly


Artifacts

5

Motivation-2 1) Epilepsy Monitoring by EEG

Purpose: • Neural prostheses • Enabling people with injury/brain disease to communicate with real world

Challenges: • Less accuracy in BCI classification in presence of Artifacts => Leads to Unintentional control of BCI

device

Purpose:

• 2% World Population Suffer from Epilepsy Seizure

• Diagnosis/Detection of Epilepsy Seizure by Long-term

EEG Monitoring (up to 72 hours)

• Early warning of seizures (prediction) onset in order to

stop seizure

• Offline processing of epilepsy patient data

Challenges:

• Seizure masked by artifacts Lead to misdiagnosis • False alarms

2) EEG based BCI

BCI is a direct link between human brain and an external computerized device bypassing the injured/diseased pathway

6

An epileptic seizure is a brief episode of signs or symptoms due to abnormal excessive or

synchronous neuronal activity in the brain.

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]

• Increase false alarms in epileptic seizure detection [5]

• Mistakes in BCI classifications


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After BPF of In Vivo data from 300 Hz to 5 kHz

False Spike detection

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Local Field Potential

[4] [5]

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Common Target: Detect and remove artifacts as much as possible without distorting signal of interest.


Thesis Objectives:

Objectives

• To investigate artifacts present at in-vivo neural recordings: characterize them and observe

the change in dynamic range.

• To propose an automated artifact detection and removal algorithm for reliably remove artifacts from in-vivo neural recordings without distorting signal of interest

• To synthesize an artifact database for quantitative performance evaluation of any artifact removal method.

• To propose application-specific artifact removal methods for scalp EEG recordings • Epilepsy seizure monitoring and detection purpose

• BCI studies/experiment purpose

• To observe the after-effect of artifact removal on later-stage neural signal processing. i.e. • Improvement in neural spike detection (in-vivo)

• Improvement in epileptic seizure detection (EEG)

• Improvement in BCI classification (EEG)

8

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

EEG Artifact Handling:

1) Avoidance 2) Detection 3) Rejection 4) Removal Existing Methods

Blind Source Separation

- ICA, CCA

- Offline and manual intervention, at best semi-automatic, suitable for global artifacts

- Assumptions to be independent or un-correlated

- Convergence problem for ICA

- Residual neural signals

Filtering/Regression

- Adaptive filtering

- Reference channel to record artifact/clean data)

Time Series Analysis

- STFT

- uniform time-freq resolution

- Wavelet Denoising

- Choices of threshold, mother wavelet and decomposition level, DWT

Empirical Technique

- HHT, e.g. EMD or EEMD (Computational complexity higher, slow)

Hybrid Methods

- Wavelet-enhanced ICA/CCA, EEMD-ICA/CCA

- Identification of artifactual component is a tough job, DWT involved, EEMD requires high computation power

9

BSS

Adaptive Filter

Summery of Existing EEG Artifact Removal Methods

– Not suitable for in-vivo neural data

– Single artifact type

– Reference channel (EOG, eye tracker, ECG, gyroscope, accelerometer, etc.)

– Mostly general purpose

– Often Manual or Semi-automatic

– Often suitable for Multi channel

– Real-time/Online processing capability

– Not enough quantitative evaluation

– Often after-effects not reported

– Lack of adequate dataset used

– Often hybrid methods (wICA, EEMD-CCA, etc.)


10

Artifact Sources

Artifacts may generate from 3 general factors :

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


11

Artifact Characterization


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Global Artifacts

Irregular/Local Artifacts Periodic Artifacts

Perspective Artifact Category/Class

Repeatability Irregular/No Periodic/Regular/Yes

Origin Internal External

Appearance Local Global

12

4-Types of Artifacts

(Identified by Empirical Observations Based on Real Neural Sequence, there could be many other types as well)

In-Vivo Artifacts

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


LFP => 0.1 Hz ~ 200 Hz, 0.1 ~ 1 mVpp

Neural Spikes => 300 Hz ~ 5 kHz, 40 ~ 500 uVpp

Artifacts => 0 ~ 10 kHz or even higher, max amplitude as high as 20 mVpp. (From real data observation)

2 Possible bands for Artifact Detection

1) 150-400 Hz (BPF) 2) >5 kHz (HPF)

13

In-Vivo Artifacts

Dynamic Range Study


Subject

(Fs in kHz)

B.W.

No of Data

Sequences

(Data Length

in min)

Amplifier Circuit

Noise Floor

(µV rms)

DR without

Artifact

(Mean ± SD)

(Full Spectrum Data

in 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 in

dB)

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

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(18)

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34.45 ± 3.42

64.36 ± 3.42

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4.605

55.75 ±

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26.92

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Full Spectrum Data with T2 art

Spike Data with T2 art





Full Spectrum DRWithout Artifact

Spike DRWithout Artifact

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In-Vivo Artifacts

Algorithm Design-1: Artifact Detection and Removal from In-Vivo Neural Data

Purpose of Algorithm

Minimum (or almost no) distortion to neural signal

Remove artifacts as much as possible

Should be automatic

Robustness is important

Should work in both single and multi-channel analysis

Should not depend on artifact types.

Approach to design algorithm:

• Use of Spectral Char. of In-Vivo Neural Signal: Potential regions for artifact detection are

– BPF: 150-400 Hz (Least LFP and Spike Power)

– HPF: >5 kHz (Noise floor)

• Stationary Wavelet Transform for decomposing neural data (multi-resolution analysis)

– ‘Haar’ as mother wavelet (simplest and useful to track sharp/transition changes in signal)

– 10-level decomposition (depends on Fs)

– Improved/Modified typical threshold value

– Garrote threshold


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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)

Scale Mother Wavelet

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<1: compress the signal

16

Why Wavelet Transform:


Good time-frequency resolution

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

Easy to implement [complexity: DWT-> O(N); FFT -> O(N log2 N);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.

Why SWT Preferred over DWT or 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 of information, preserves spike data and doesn’t 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 L log2N)].

N = length of signal, L = decomposition level

Digital implementation of SWT: A 3 level SWT filter bank and SWT filters

17

Proposed Algorithm-1 (In-Vivo Data)


Raw Artifactual Neural Data

Artifact-free Neural Data

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Detection Stage

Results to Support “Why SWT” ?


FPR

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

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Spike data comparison after artifact removal

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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)

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Effect of Filtering

– Separate spikes from artifacts


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Only

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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 D3, D4, D5, D6 => Spikes. D8, D9, D10 and A10 => LFP

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


Hard Garrote Soft

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Data Synthesis for Simulation


23

Performance Evaluation (Important Definitions)

Simulation is performed on both real and synthesized (semi-simulated) signal database from different subjects.

Removal Measurement

Lambda, 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

Tart = Time duration of artifact

Ttotal = Total data length

Artifact SNR: Consider artifact as signal and neural signal as noise:

24

Results (Tested on Synthesized Sequence)


25

SNDR Improvement

Results (Tested on Real Sequence)


Data Sample 1: Rat Hippocampus

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Data Sample 2: Rat Hippocampus

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Quantitative Evaluation


Amount of Artifact Removal Measurement

Amount of Distortion Measurement

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Comparison with Other Methods


In terms of Spike Detection Improvement

In terms of Performance Metrics

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Algorithm Design-2: Artifact Detection and Removal from EEG for Epilepsy Seizure Monitoring

Challenges: 3 Signal components to differentiate:

1) EEG Rhythms

2) Artifacts and

3) Seizure Events

Approach: • Utilizing Seizure activities’ spectral band into consideration

– 0.5-29 Hz (HPF at 30 Hz gives non-seizure events)

• A Reference Seizure epoch (either real or simulated) is matched to double check whether artifact or seizure

• Epoch-by-epoch processing – Determination of epoch length is crucial

• SWT based denoising – 8-level decomposition

– Similar threshold value modification


29

Proposed Algorithm-2 (For EEG-based Seizure Detection)


30

Methods


Signal Synthesis

Data Collection • Real epilepsy patient data from CHB-MIT database

• Simple EEG experiments performed for recording particular artifact(s) • Eye blink/ Eye movement

• Chewing/Swallowing

• Head/Hand Movement Seizure Detection Flow

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Qualitative Results


Real data

Simulated Data

6 Artifact Types

(Zoom-in)

32

Improvement in Seizure Detection


False alarms improvement

33


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EEG Features before and after Artifact Removal

Features Extracted: (i) Entropy (ii) Kurtosis (iii) Line Length (iv) Peak (v) NEO (vi) Variance (vii) FFT (viii) FFT Peak

Note: The features between seizure and non-seizure data are more separable after artifact removal which suggests that it increases the detection rate and minimizes false alarms (false alarms are due to artifacts).

Improvement in Seizure Detection (Cont…)

Algorithm Design-3: Artifact Detection and Removal from EEG for BCI

Scalp EEG-based BCI is the most widely used BCI studies 1. P300 ERP (Event Related Potential)

2. MI (Motor Imaginary)

3. SSVEP (Steady-state Visual Evoked Potential)

Challenges

Difficult to avoid artifacts during BCI experiments

Approaches – Unique idea of Artifact Probability Mapping

– Epoch by epoch processing

– SWT-based denoising

– Consideration of type of BCI to utilize desired signal band(s) for artifact identification.

Presented By Md Kafiul Islam

([email protected]) 35

Proposed Algorithm-3 (For EEG-based BCI)


Entropy -> Randomness Kurtosis -> Peakedness Skewness -> Symmetry Periodic waveform index (PWI) -> Periodicity

36

Denoise Based on type of BCI Study

Methods


Signal Synthesis

Data Collection • BCI Competition-IV EEG dataset-1/2a/2b

• Simple EEG experiments performed for recording particular artifact(s) • Eye blink/ Eye movement

• Chewing/Swallowing

• Head/Hand Movement BCI Classification Flow (MI study)

Artifact Removal

Feature Extraction

(Windowed Means)

LDA Classifier

BCILAB Tool used for BCI Performance Evaluation

37

Qualitative Results


Simulated data

Real data

38

Quantitative Results


BCI Performance Improvement

SNDR Improvement

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Comparison of Current EEG Artifact Removal Techniques With Proposed Ones

EEG Artifact Removal for Seizure Detection EEG Artifact Removal for BCI

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Summary of Contributions

• Investigation on In-Vivo Neural Artifacts (for the very First Time) – Identifying artifact sources – Characterizing them in to 4 types – Studied change in dynamic range

• Artifact Database Synthesis – Allowing realistic artifact simulation in real clean neural signals – Quantitative performance evaluation becomes possible

• Unique Artifact Probability Mapping – Gives user the freedom to select probability threshold – Applicable to other EEG applications

41


Summary of Contributions (Cont..)

• Proposed 3 different artifact removal algorithms (First time for in-vivo neural data)

– Almost no distortion to neural signal of interest – Doesn’t depend on artifact types – Application specific solution – Can work for both single and multi-channel neural data – Parameters can be optimized for best performance – Straightforward parameter adjustment. – Automatic algorithm / Minimal manual intervention (during initial training

parameters) – Suitable for both online and offline processing – Unique idea of artifacts probability mapping for EEG epochs – All three algorithms’ performances have been evaluated both qualitatively and

quantitatively. – Compared with other existing competing methods and ours found to be

superior – Open source codes available for everyone to use and edit for further

improvement(s). – Reproducible research

42 Presented By Md Kafiul Islam

([email protected])

Future Directions-1

Improvements on Current Algorithms

1) In-Vivo Neural Data

– Complexity reduction and Optimizing the algorithm further to allow faster

processing and less storage.

– Automatic Parameter Adaptation

– Proceed to hardware implementation and perform real-time experiments to

verify the actual performance in practice.

2) EEG Applications

– Online Processing

– Validation with Patient/User Data

– Further Optimization and Tuning


43

Future Directions-2

Other Potential Applications

1) Other Neural Signals

– Artifact removal from ECOG/iEEG and sub-scalp EEG data epilepsy seizure monitoring

– Motion artifact removal in ambulatory EEG monitoring

– Artifact removal from Peripheral nerve recordings for neural prostheses applications

– Metallic interferences/artifact removal from MEG

– Stimulation artifact removal during DBS

2) Non-Neural Biomedical Signals – Artifact removal from ambulatory ECG or PCG for wearable healthcare monitoring

applications

3) Software GUI for Complete Solution

– Signal-specific artifact removal

» EEG, iEEG, in-vivo, sub-scalp EEG, etc.

– Application-specific artifact removal

» Epilepsy, BCI, Sleep studies, Alzheimer diagnosis, Mental fatigue & depression studies, etc.



Conclusion

• First time (to best of knowledge) Investigation of artifacts for in-vivo neural data – Useful for future neuroscience studies

• Application-specific EEG artifact removal – Enhanced later-stage signal processing performance

• Open Artifact database and MATLABT source codes – Reproducible research by continuing and improving current

algorithms

– More reliable performance evaluation of any artifact removal methods

• Future brain research and clinical applications may find our work useful.


45

Acknowledgments

I would like to thank – My supervisor for his helps, encouragements and support.

– My thesis committee for invaluable comments during my QE and on my thesis.

– My lab mate Jules, Xu Jian, Zhou Yin, and Reza for their help and support

– Dr Amir Rastegarnia for his feedback and help on my papers and thesis

– All my friends and colleagues in VLSI Lab for making a nice working environment.

– All my friends who have helped and encouraged me during my PhD course.

46

Publications Published/In-Press (Journal):

1. M. K. Islam, A. Rastegarnia, A. T. Nguyen, and Z. Yang, “Artifact Characterization and Removal for In- Vivo Neural Recording,” Journal

of Neuroscience Methods, vol. 226, no. 0, pp. 110 – 123, 2014. (Chapter-2 + Chapter-4)

2. M. K. Islam, A. Rastegarnia, and Z. Yang, “A Wavelet-Based Artifact Reduction from Scalp EEG for Epileptic Seizure Detection”,

Published online (In Press) in IEEE Journal of Biomedical and Health Informatics, 2015. (Chapter-5)

3. Jian Xu, Menglian Zhao, Xiaobo Wu, Md. Kafiul Islam, and Zhi Yang, “A High Performance Delta-Sigma Modulator for Neurosensing”

– Sensors 2015, 15(8), 19466-19486; doi:10.3390/s150819466. (Chapter-2)

In-Preparation/Submitted (Journal):

1. M. K. Islam, A. Khalili, and Z. Yang, “Probability Mapping based Artifact Detection and Wavelet Denoising based Artifact Removal from

Scalp EEG for Brain-Computer Interface (BCI) Applications,” In Preparation for submission to Journal of Neuroscience Methods, 2015.

(Chapter-6)

2. M. K. Islam, and Z. Yang, “Artifact Characterization, Detection and Removal from Scalp EEG - A Review,” In Preparation for submission to

IEEE Reviews in Biomedical Engineering, 2015. (Chapter-3)

3. M. K. Islam, and Z. Yang, “Unsupervised Selection of Mother Wavelet and Parameter Optimization during Wavelet Denoising Based

Artifact Removal from EEG Signal” – Submitted to the Journal of Signal Processing Systems, Springer, 2015. (Chapter-5)

Published (Conference):

1. Islam MK, Tuan NA, Zhou Y, and Yang Z. “Analysis and processing of in vivo neural signal for artifact detection and removal”. In:

BMEI – 5th International Conference on Biomedical Engineering and Informatics; 2012. p. 437–42. (Chapter-2 and Chapter-3)

1. Xu, J., Islam, M. K., Wang, S., and Yang, Z. “A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural

recording”. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE (pp. 2764-

2767). IEEE. (Chapter-2)



The End

Q & A

Thank You


48

Healthcare

PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS"