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

2Type 1

0 2 4 6 810

-5

0

5Type 0

Artifact Characterization

0 0.5 1-2

-1

0

1

m p l i t u 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

100

101

-60

-40

-20

0

20

40

60

Freq, kHz

S N D R , d B

SNDR BeforeSNDR After

4 6 8 10 12 14 16 18 2010-2

100

102

104

106

Artifact SNR in dB

S p e c t r a l D i s t o r t i o n

ArtifactualReconstructed

5 10 15 200

20

40

60

80

100

Artifact SNR in dB

% a r t i f a c t r e d u c t i o n , l a m d a

4 6 8 10 12 14 16 18 200

0.05

0.1

0.15

0.2

Artifact SNR in dB

R M S E

ArtifactualReconstructed

5 10 15 200

5

10

15

20

25

30

Artifact SNR in dB

d e l S N R i n 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 l i t u d e , m V

ArtifactualReconstructed

2.6 2.8 3 3.2 3.4 3.6 3.8 4

-0.1

0

0.1

A m p l i t u d e , m V LFP

RealReconstructed

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 r m a l i z e A m p l i t u d e

Spike Data

RealReconstructed

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

Am

plitu

de, m

V

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