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

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ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

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

  1. 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. 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. 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 -800 -600 -400 -200 0 200 400 600 8.8 9 9.2 9.4 9.6 9.8 10 x 10 5 -3000 -2000 -1000 0 1000 2000 3000 LFP Spike s 3 Single-multi unit
  4. 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. 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. 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. 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 0.5 1 1.5 2 x 10 -3 Time Sample Voltage,V [1] 260 265 270 275 280 285 290 295 -15 -10 -5 0 5 x 10 -4 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. 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. 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. 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. 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. 12. Artifact Characterization Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) 0 2 4 6 8 10 -10 -5 0 5 0 2 4 6 8 10 -10 -5 0 5 0 2 4 6 8 10 -10 -5 0 5 SignalAmplitude,mV 0 2 4 6 8 10 -5 0 5 0 2 4 6 8 10 -5 0 5 0 2 4 6 8 10 -4 -2 0 2 0 2 4 6 8 10 -4 -2 0 2 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. Data Synthesis for Simulation Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) 23
  24. 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. 25. Results (Tested on Synthesized Sequence) Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) 25 SNDR Improvement
  26. 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. 27. Quantitative Evaluation Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) Amount of Artifact Removal Measurement Amount of Distortion Measurement 27
  28. 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. 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. 30. Proposed Algorithm-2 (For EEG-based Seizure Detection) Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) 30
  31. 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. 32. Qualitative Results Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) Real data Simulated Data 6 Artifact Types (Zoom-in) 32
  33. 33. Improvement in Seizure Detection Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) False alarms improvement 33
  34. 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. 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. 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. 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. 38. Qualitative Results Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) Simulated data Real data 38
  39. 39. Quantitative Results Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) BCI Performance Improvement SNDR Improvement 39
  40. 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. 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. 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. 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. 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. 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. 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. 46
  47. 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. 48. The End Q & A Thank You  Presented By Md Kafiul Islam (kafiul_islam@u.nus.edu) 48

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