Artifact Detection & Removal

What i s Ar t i fac t

Sources o f Ar t i fac t

Proper t ies o f Ar t i fac t

Problems w i th Ar t i fac t

Art i fac t Detect ion

Poss ib le Ar t i f ac t Remova l Techn iques

Some Observat ions

What is Artifact…!?

In neural recording, artifacts are interfering signals that originate from some source other than the brain of interest.

In Vivo Extracellular Recording of Spontaneous Neural Activity

Offending artifacts may obscure, distort, or completely misrepresent the true underlying recorded signal being observed.

Possible Artifact Sources

During in vivo extracellular neural recording, animal’s movements causes motion artifacts in the recorded neural signal.

In addition to artifacts generated by the body, many artifacts originate from outside the body. Movement by the animal, or even just settling of the electrodes, may cause electrode pops, spikes originating from a momentary change in the impedance of a given electrode.

The length of the interconnect between the high-impedance recording sites (1–3 MΩ at 1,000 Hz) and the preamplifier input is critical because it serves as an "antenna" and introduces large slow-frequency artifacts, originating from movement of the electrically charged whiskers, cable movements, fast head movement-associated "microphonia," and other environmental noise sources.

Properties of ArtifactsUsually the artifacts have very large magnitude compared to the neural data of interest, i.e. spike and local field potential.

The frequency range for artifact may vary from very low (motion artifact) to high frequency (artifact due to residue charge) range.

Hence it is not possible to remove simply by filtering as the frequency range of recorded neural data can be from sub Hz (i.e. LFP) to several kHz (neural spikes).

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Problems with Artifacts

• Can cause electronics saturation• High dynamic range required (Higher ENOB in ADC)

• Mislead to spike detection (high freq)• Misinterpretation for LFP recording(slow freq)

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After BPF of In Vivo data from 300 Hz to 5 kHz

False Spike detection

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Local Field Potential

Artifact Detection

High frequency artifact detection by High Pass Filtering

Low frequency artifact detection by counting zero crossings after Low Pass Filtering

Mean/RMS adjustmentAdaptive filteringMeasuring Slope for flat artifacts

High Freq Artifact: Detection & Removal

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high freq artifact

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In vivo data after high pass filtered at 5 kHz

High freq artifact

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In vivo data after high pass filtered at 5 kHz and artifact removed

HPF at 5kHz

HPF at 5kHz

Removal of Slow Artifacts…Not Impressive at all…!!!

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Loss of 3 sec data..!

Flat Artifacts: Can be detected by measuring slope!

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Flat Artifacts: cont…

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final signalafter remove zero slope valueIn vivo signal

Calculate slope of the

data

Remove data points of zero

slope

Remove high freq artifacts

Artifact free data

In-vivo data

Artifact Remove: Mean Adjusting

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Detection of Artifact

original dataafter BPF from 50Hz to 500Hz

Artifact Detected

Computational Time is Huge! Also not Efficient…!!???

Can be detected two ends by BPF at 50-500Hz…!!?? So not a concern for Spike

detection, but Field Potential..!???

Artifact components of 50Hz and its harmonicfrequencies…!!!

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X: 150Y: 1.012e-007

Single-Sided Amplitude Spectrum

Frequency (Hz)

|X(f

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X: 174.8Y: 9.911e-008

X: 250Y: 1.08e-007 X: 349.9

Y: 1.03e-007

X: 399.9Y: 1.035e-007

X: 420Y: 6.497e-008

X: 449.9Y: 8.92e-008

X: 539.9Y: 6.163e-008

X: 549.9Y: 7.699e-008

X: 649.9Y: 6.105e-008X: 699.9

Y: 5.539e-008

F F T

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Some Observations…

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X: 250Y: 7.06e-008

Single-Sided Amplitude Spectrum

Frequency (Hz)

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Y: 6.817e-008

X: 399.9Y: 6.379e-008

X: 499.9Y: 6.265e-008

X: 549.9Y: 5.426e-008

X: 420.1Y: 8.382e-008

X: 599.9Y: 4.649e-008

FFT

Some Observations

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FFT

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Log Frequency, Hz

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Some Observations

BPF from 400 Hz to 5 kHz

In Vivo Data

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HPF at 5 kHz

Artifact Removing by RMS Comparison

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Artifact

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Smoothing Data to get Envelope

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Remove the Envelope…Not Perfect though..!

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Adaptive Filtering: Basics

Adaptive Method for Artifact Detection based on Prediction Error Value

Change over Switch

Swit

ch

Z-1Input

Weight FIR Filter

∑

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Output

+

Input

Decision Device

Detection Threshold

e

Adaptive Filtering: Flow Chart

1) x’k = wT xk-L

2) ek = xk - wT xk-L

3) wk+1 = wk + 2µ sign(ek) xk

4) µk+1 = αµk + γek2

(0<α<1 & γ>0)

5) εk = ∑i=1k λk-1 ei

2

ei = xi - wkT xi-L

Adaptive Filtering: Simulation Result

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Output of Adaptive FilterInput of Adaptive Filter

Predicted Value

Wrong prediction!

?