This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.

1. 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

2. 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
(kafiul_islam@u.nus.edu)
2

3. Background-1: In-Vivo Neural Signals
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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
x 10
6
-1400
-1200
-1000
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-600
-400
-200
0
200
400
600
8.8 9 9.2 9.4 9.6 9.8 10
x 10
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-3000
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0
1000
2000
3000
LFP
Spike
s
3
Single-multi unit

4. Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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

5. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Artifacts
5

6. 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.

7. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10
5
-2
-1.5
-1
-0.5
0
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1.5
2
x 10
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Time Sample
Voltage,V
[1]
260 265 270 275 280 285 290 295
-15
-10
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0
5
x 10
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Time, Second
Voltage,Volt
[2]
260 265 270 275 280 285 290 295 300
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
x 10
4
Time, Second
Voltage,Volt
After BPF of In Vivo data from 300 Hz to 5 kHz
False Spike detection
[3]
9.06 9.08 9.1 9.12 9.14 9.16 9.18 9.2
x 10
4
-15
-10
-5
0
5
x 10
-5
Time, Second
Voltage,Volt
Local Field Potential
[4] [5]
7
Common Target: Detect and remove artifacts
as much as possible without distorting signal of interest.

8. Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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

9. 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

10. 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.)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
10

11. 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.)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
11

12. Artifact Characterization
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
0 2 4 6 8 10
-10
-5
0
5
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SignalAmplitude,mV
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-5
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5
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2
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Time, Sec
0 2 4 6 8 10
-2
0
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
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

13. Properties of Artifacts
(Comparison in Spectral Domain with Neural Signal of Interest)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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

14. Dynamic Range Study
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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
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
45
50
55
60
65
70
75
80
85
90
Artifact Amplitude, mV
DynamicRange,dB
Full Spectrum Data with T2 art
Spike Data with T2 art
Full Spectrum Data with T1 art
Spike Data with T1 art
Full Spectrum Data with T3 art
Spike Data with T3 art
Full Spectrum DR
Without Artifact
Spike DR
Without Artifact
14
In-Vivo Artifacts

15. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
15

16. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
      dt
s
t
tx
s
ss 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

17. Why Wavelet Transform:
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
 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

18. Proposed Algorithm-1 (In-Vivo Data)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Raw Artifactual
Neural Data
Artifact-free
Neural Data
18
Detection Stage

19. Results to Support “Why SWT” ?
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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
NormalizedAmplitude
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)
19

20. Effect of Filtering
– Separate spikes from artifacts
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
0 1 2 3 4 5 6 7 8
-1000
-500
0
500
Real Data from Monkey Front Cortex
0 1 2 3 4 5 6 7 8
-1000
-500
0
500
Amplitude
0 1 2 3 4 5 6 7 8
-1000
-500
0
500
Time, Sec
Original
Reconstructed
by only SWT
Reconstructed by
SWT + Filtering
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ROC for Spike Detection
FPR
TPR
SWT + Filtering
Only
20

21. Threshold Value
• Universal Threshold:
Wi = Wavelet coefficients; ơi = variance of Wi; N = length of signal
• Modified Threshold:
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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
21

22. 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.
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Hard GarroteSoft
22

23. Data Synthesis for Simulation
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
23

24. 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:
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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

25. Results (Tested on Synthesized Sequence)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
25
SNDR Improvement

26. Results
(Tested on Real Sequence)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Data Sample 1: Rat Hippocampus
0 0.5 1 1.5 2 2.5 3 3.5 4
x 10
5
-8
-6
-4
-2
0
2
4
Recorded vs Reconstructed (Before & After Artifact Removal)
Time Sample
SignalAmplitude,mV
Reconstructed
Recorded
Data Sample 2: Rat Hippocampus
26

27. Quantitative Evaluation
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Amount of Artifact Removal
Measurement
Amount of Distortion
Measurement
27

28. Comparison with Other Methods
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
In terms of Spike Detection Improvement
In terms of Performance Metrics
28

29. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
29

30. Proposed Algorithm-2
(For EEG-based Seizure Detection)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
30

31. Methods
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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
31

32. Qualitative Results
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Real data
Simulated Data
6 Artifact
Types
(Zoom-in)
32

33. Improvement in Seizure Detection
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
False alarms improvement
33

34. Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
34
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…)

35. 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
(kafiul_islam@u.nus.edu)
35

36. Proposed Algorithm-3
(For EEG-based BCI)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Entropy -> Randomness
Kurtosis -> Peakedness
Skewness -> Symmetry
Periodic waveform index (PWI) -> Periodicity
36
Denoise Based on
type of BCI Study

37. Methods
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
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

38. Qualitative Results
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
Simulated data
Real data
38

39. Quantitative Results
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
BCI Performance Improvement
SNDR Improvement
39

40. Comparison of Current EEG Artifact Removal Techniques With
Proposed Ones
EEG Artifact Removal for Seizure Detection EEG Artifact Removal for BCI
ComputationalTimePerformanceMetricsValue
40

41. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)

42. 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
(kafiul_islam@u.nus.edu)

43. 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
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
43

44. 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.
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
44

45. 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.
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
45

46. 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.
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47. 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)
Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
47

48. The End
Q & A
Thank You

Presented By Md Kafiul Islam
(kafiul_islam@u.nus.edu)
48