Background In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest. New method The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed. Results Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio. Comparison with existing methods Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms. Conclusion Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.

1. Artifact Characterization, Detection
and Removal for In-Vivo Neural
Recording
Presented By:
Md Kafiul Islam
PhD Candidate
Supervisor: Dr. Zhi Yang
Translational System and Signal Processing Group
Department of Electrical and Computer Engineering
National University of Singapore
Presented By Md Kafiul Islam 1

2. 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
Presented By Md Kafiul Islam 2

3. Motivation
In-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

4. Motivation
Wireless Neural Recording and Signal Processing System
Presented By Md Kafiul Islam
A
Analog
Front Ends
Electrode
Array
ADC
On-chip Neural Signal
Processing
Telemetry
Interface
(Bidirectional)
· Offset Remove
· Power line
Interference Remove
· Artifact Remove
Signal Pre-
Processing
Neural Signal
Processing
(LFP + Spikes)
· Feature Extraction
· Classification
· Compression
4

5. Artifact Sources and Properties
Interfering signals that originate from source other than brain of
interest.
Presented By Md Kafiul Islam
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
5

6. 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.
Presented By Md Kafiul Islam
Global Artifacts
Irregular/Local
Artifacts
Periodic Artifacts
Perspective Artifact Category/Class
Repeatability Irregular/No Periodic/Regular/Yes
Origin Internal External
Appearance Local Global
6

7. Artifact Types and Spectral Characteristics
Presented By Md Kafiul Islam
Template Extract
7

8. 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
Presented By Md Kafiul Islam 8

9. Proposed Solution
Presented By Md Kafiul Islam
 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.
Artifactual
Data
Artifact-
free Data
9

10. Why SWT ?
SWT :
 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)
 Computational complexity is between DWT
and CWT.
DWT:[O(N)] SWT[O(NL)] CWT [O(N L log2N)]
N = length of signal, L = decomposition level
Presented By Md Kafiul Islam
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 (thresholding
and reconstruction by inverse transform)
of artifacts.
10
Digital implementation of SWT:
A 3 level SWT filter bank and SWT filters A 2-Level DWT decomposition and the
reconstruction structures

11. Why SWT (2)… ?
Presented By Md Kafiul Islam
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)

12. Effect of Filtering
– Separate spikes from high frequency artifacts (e.g. type-3)
Presented By Md Kafiul Islam
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
Amplitude
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 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
False Positive Rate (FPR)
TruePositiveRate(TPR)
SWT Only
SWT + Filtering
12

13. Threshold Value
• Universal Threshold:
Wi = Wavelet coefficients; ơi = variance of Wi; N = length of signal
• Modified Threshold:
Presented By Md Kafiul Islam
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.

14. 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
Hard GarroteSoft

15. Data Synthesis for Simulation
Presented By Md Kafiul Islam
Clean in-vivo
Data (Reference)
Raw In-Vivo Data
With Artifacts
Extract Artifact
Templates
Synthesized
Artifactual Data
Random
AmplitudeRandom
Location
Random
Duration

16. Performance Evaluation
(Important Definitions)
Simulation is performed on both real and
synthesized (semi-simulated) signal database
from 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:
Presented By Md Kafiul Islam
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:
16

17. Removal Results (Tested on Synthesized Sequence)
Presented By Md Kafiul Islam 17

18. Presented By Md Kafiul Islam 18
Removal Results (Tested on Real Sequence- Rat Data)

19. Presented By Md Kafiul Islam 19
Removal Results (Tested on Real Sequence-Monkey Data)

20. Results (Tested on Synthesized Sequence)
Presented By Md Kafiul Islam 20

21. Quantitative Evaluation
Presented By Md Kafiul Islam 21

22. Comparison with Other Methods
Presented By Md Kafiul Islam
dB
dB
Artifact Artifact
Artifact Artifact
dB
dB
22

23. 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
Presented By Md Kafiul Islam 23

24. 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
Presented By Md Kafiul Islam 24

25. The End
Q & A
Thank You

Presented By Md Kafiul Islam 25