This paper analyzes neural signal recording artifacts and proposes a method for artifact detection and removal. It characterizes artifacts into four types based on their frequency characteristics and develops algorithms to detect and remove each type. Testing shows the method achieves over 80% detection accuracy for artifacts comparable to neural spikes. It also improves the signal-to-distortion ratio by 10-30 dB after removing artifacts.

1. Analysis and Processing of In-Vivo Neural Signal for Artifact
Detection and Removal
Md Kafiul Islam, Nguyen A. Tuan, Yin Zhou, and Zhi Yang
Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore
Paper ID: P3900
Contact email:
kafiiut@gmail.com
2. Artifact Sources and Types
Artifacts can be classified into external and internal types according to their
origin. Internal artifacts arise from body activities that are either due to
movements made by the subject itself or sudden changes of bioelectrical
potentials, while external artifacts result from coupling with unwanted external
interferences.
we characterize majority of the examined artifacts into four types based on
frequency characteristics, sharpness of edge, duration, shape and size. They are:
i) Type-0 ii) Type-1 iii) Type-2 and iv) Type-3 as shown in the below figure.
3. Method
We have manually identified different artifact segments and labeled each into
one of the four artifact categories as shown in figure 1. Artifact envelopes are
further extracted from the data segments to get the templates and artifact
spectrum estimation is performed using windowed Fourier Transform.
Fig. 2. (a), (b), and (c): manually labeled type-1, type-2, and type-3 artifact segments
collected from recorded data. (d), averaged type-1 and type-2 artifact power spectrum in
comparison with LFP. (e), type-3 artifact spectrum and spike spectrum. Spikes are
randomly selected from two different templates.
1. Introduction
Frequently, neural data are severely corrupted by various recording artifacts,
especially in less constrained experiment environment. The appeared
artifacts sometimes can cause mistakes in interpretation of information.
Therefore, for proper information decoding, the detection and removal of
such artifacts play a vital role.
This paper analyses different types of artifacts that appear in neural recording
experiments and thus a method is proposed to detect and remove
artifacts as a part of preprocessing procedures before information
decoding. Through modeling and data analysis, we reason that artifacts
have different spectrum statistics compared with field potentials and
spikes and the frequency bands of 150-400 Hz and >5 kHz are the most
prospective regions to detect artifacts. A synthesized database based on
recorded neural data and manually labeled artifacts has been built to
allow quantitative evaluations of the proposed algorithm. Testing results
have shown that over >80% positive detection ratio is achievable for
artifacts with magnitude comparable to neural spikes. Quantitative signal-
to-distortion ratio (SDR) simulation has shown that it is possible to have
10-30 dB SDR improvement at waveform segments that contain artifacts.
6. Results
Artifact detection results at different artifact SNR are shown in figure 5. Averaged
true positive rate above 80% is achievable when artifacts have comparable
magnitude with neural spikes.
The performance of the removal algorithm has been evaluated in terms of SDR
(Signal-to-Distortion Ratio) improvement for each artifact segments
5. Artifact Removal
After artifacts have been identified and categorized, different removal approaches
are applied according to the artifact type with the goal to minimize the added
distortion to neural data.
High frequency artifacts, i.e. type-3, are eliminated by replacing artifactual samples
with a straight line connecting two ends of the artifactual segment, as shown in
figure 5(d). Usually the replacement duration is less than 1ms, thus added
distortion to neural data is relatively small. Slow-artifacts, i.e. type-0, 1 and 2, are
removed by subtracting the estimated envelope.
5th International Conference on BioMedical Engineering and Informatics
(BMEI 2012)
This work is supported by Singapore Public Sector Funding R-263-000-699-305,
NUS tier-1 grant R-263-000-619-133, and SERC grant R-263-000-656-305.
Fig. 1. Illustration of different artifact sources on left and Different types of artifact
present at in-vivo recordings on right
4. Artifact Detection
The proposed artifact detection algorithm relies on the hypothesis that the power
of neural signal of interest is small in the two frequency bands of 150-400Hz and >5
KHz and so are the least overlapping regions with artifacts. Therefore, band-pass
filtering from 150-400Hz to detect relatively low frequency artifacts (i.e. type-1 or
type-2), while high-pass filtering at 5 kHz to detect high frequency artifacts (i.e.
type-3), are applied. In order to emphasize the artifact edges after performing band-
pass filtering, we take the NEO output and the result is shown in the following fig.
0 10 20 30 40 50 60 70 80
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SignalAmplitude,mV
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x 10
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Clean Data
BPF Output
NEO Output
T-1 Artifact
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SignalAmplitude(mV)
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(a)
Raw Data
Data with Synthesized Artifact
Data after Artifact Removed
Type 3Type 2
Type 1
Fig. 4. Results of removal algorithm applied on real in-vivo artifactual data for all types of artifact
shown on left and Simulation result of the overall algorithm applied to a synthesized artifactual data
shown on right.
Fig. 3. Effect of HPF at 5 KHz for detecting type-3 artifact (b) and Effect of band pass
filtering at 150-400 Hz for detecting type-1 (c) and type-2 (d) artifacts shown on left.
Illustration of BPF followed by NEO based artifact detection on synthesized T-1 artifact
superimposed to real neural data shown on right.
0 5 10 15 20 25 30
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Artifact SNR
FalsePositiveRate
T2, BPF + NEO
T2, BPF Only
T1, BPF Only
T1, BPF + NEO
T3, BPF Only
T3, BPF + NEO
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Artifact SNR
TruePositiveRate
T1, BPF+NEO
T1, BPF Only
T3, BPF+NEO
T3, BPF Only
T2, BPF Only
T2, BPF+NEO
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-10
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40
SDRImprovement(dB)
Artifact SNR ( dB )
Type 2 Artifact
Type 1 Artifact
Type 3 Artifact
Fig. 5. Detection accuracy comparison for both BPF based and BPF followed by NEO based
artifact detection methods w.r.t. different artifact SNR for different types of artifact
shown on left. SDR improvement for different artifact SNR after the removal algorithm is
applied shown on right.