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. Proposed method: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform (SWT) 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 pro-posed 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. TSS
Group
Translational System &
Signal Processing Group
Artifact Characterization and Removal for In-Vivo
Neural Recording*
M. K. Islam, A. Rastegarnia, A. T. Nguyen and Z. Yang
Abstract
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.
Proposed method: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform (SWT) 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 pro-posed 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.
Test on Synthesized Data
Proposed Algorithm
Test on Real Data-1: Monkey
Artifact
SNR
(dB)
Amount of Artifact Reduction, λ
(Ideal value = 100)
Proposed wICA wCCA ICA EMD-ICA
EMD-
CCA
5 58 30 45.5 46.45 -1.82 9
10 85 8 40 33.1 -3.8 3.4
15 90 -1 37 38.2 22.25 1.3
20 60 5 29 25.1 26 16
25 18.5 -3.5 8.85 17.25 -5.5 4.2
Artifact
SNR
(dB)
Signal SNR Improvement, ΔSNR
Proposed wICA wCCA ICA
EMD-
ICA
EMD-
CCA
5 7.5 4 0.5 6.67 0.02 0.6
10 16 6.2 5 8 0.05 2.93
15 20 5 9.8 12.3 12.4 3.05
20 18 5.5 15.2 13.9 9.5 4.58
25 17.6 5.4 13.1 13.3 3.5 5.1
Artifact
SNR
(dB)
Improve in Spectral Distortion
Proposed wICA wCCA ICA EMD-ICA
EMD-
CCA
5 49.5 - 13.03 -4.17 -1.21 -5.35 -3.5
10 184 - 23 -7 -2.2 -10.5 -9
15 4.88e3 -25 -3.95 -2.52 -1.0 -70
20 5.34e4 -36.4 -1.92 -1.4 -9.6 -37.5
25 5.65e5 -40 16.15 5.69 -164.5 -60.48
Comparison with Other Methods
SNDR Comparison
Performance Metrics vs Artifact SNR
SWT Coefficients’Sub-bands
0 1 2 3
-4
-2
0
2
Type 1
0 2 4 6 8
10
-5
0
5
Type 0
Artifact Characterization
0 0.5 1
-2
-1
0
1
m
p
litu
d
e
,
m
V
Type 2
0 0.05 0.1 0.15 0.2
-10
-5
0
5
Ti S
Type 3
Artifact Reduction Signal Distortion
10
-3
10
-2
10
-1
10
0
10
1
-60
-40
-20
0
20
40
60
Freq, kHz
S
N
D
R
,
d
B
SNDR Before
SNDR After
4 6 8 10 12 14 16 18 20
10
-2
10
0
10
2
10
4
10
6
Artifact SNR in dB
S
p
e
c
tra
l
D
is
to
rtio
n
Artifactual
Reconstructed
5 10 15 20
0
20
40
60
80
100
Artifact SNR in dB
%
a
rtifa
c
t
re
d
u
c
tio
n
,
la
m
d
a
4 6 8 10 12 14 16 18 20
0
0.05
0.1
0.15
0.2
Artifact SNR in dB
R
M
S
E
Artifactual
Reconstructed
5 10 15 20
0
5
10
15
20
25
30
Artifact SNR in dB
d
e
lS
N
R
in
d
B
Test on Real Data-2: Rat
0 1 2 3 4 5 6
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
Time, Sec
A
m
p
litu
d
e
,
m
V
Artifactual
Reconstructed
2.6 2.8 3 3.2 3.4 3.6 3.8 4
-0.1
0
0.1
A
m
p
litu
d
e
,
m
V
LFP
Real
Reconstructed
3.506 3.508 3.51 3.512 3.514 3.516 3.518 3.52 3.522 3.524 3.526
-8
-6
-4
-2
0
2
4
Time, Sec
N
o
rm
a
liz
e
A
m
p
litu
d
e
Spike Data
Real
Reconstructed
0 1 2 3 4 5 6 7 8 9 10
-6
-4
-2
0
2
0 1 2 3 4 5 6 7 8
-6
-4
-2
0
2
Amplitude,mV
Time, Sec
Type-2
Artifact
Type-3
Artifact
*Md Kafiul Islam, Amir Rastegarnia, Anh Tuan Nguyen, Zhi Yang: Artifact Characterization and Removal for In-Vivo Neural Recording. Journal of Neuroscience Methods
04/2014; 226(C):110-123.
Artifact
SNR
(dB)
Improve in Temporal Distortion, RMSE
(Root Mean Square Error)
Proposed wICA wCCA ICA
EMD-
ICA
EMD-
CCA
5 0.02 0.06 2.1e-3 0.023 1.6e-4 3.6e-3
10 0.044 0.082 -0.05 0.037 0.002 7e-3
15 0.102 0.10 0.07 0.085 0.08 0.016
20 0.17 0.098 0.145 0.164 0.137 0.06
25 0.32 0.114 0.28 0.243 0.031 0.12