1. Automated Reduction of Non-neuronal
Signals from Intra-cortical Microwire
Array Recordings by Use of
Correlation Technique
Kunal J. Paralikar1
, Chinmay Rao2
, and Ryan S. Clement1
8/21/08
30th
Annual International Conference of the
IEEE Engineering in Medicine and Biology Society
1
Department of Bioengineering and 2
Department of Electrical Engineering
The Pennsylvania State University
2. Outline
• Statement of Problem: common noise
• Basic Approach
– Inter-electrode Correlation (IEC) algorithm
– Performance metrics: Mean-spike
• Initial Evaluation Study
• Comparison Study
– Differential ref, virtual ref, IEC w/ varying levels of
common noise
• Conclusions
3. A
B
0
1
2
3
4
5
6
R44 R46* R47 R48 R50 R51
Subject
MaxForce(mN)
Collagenase Sites
Control Sites
coll Non-coll
Average
p < 0.015
A B
Days Post-Implant
4. 0 2 4 6 8 10 12 14 16 18 20
-80
-60
-40
-20
0
20
40
60
uVolts
msec
Model-based analysis of cortical recording with silicon
Microelectrodes (Michael A. Moffitt, Cameron C. McIntyre*)
Intracortical Microelectrode Recordings
Univ. of Utah
6. 0 2 4
Time (s)
200
0
-200
Multi-channel Recording Sample
Voltage(µV)
Common Noise- A Common Problem
Common Noise Sources:
- Movement artifacts
- Electric fields
- Physiologic (LFPs, EEG, EMG)
8. Omnetics
connector
dental acrylic
skull bone
cortex
(motor area)
1mm
50 µm
250 µm
250 µm
polyimide-coated
tungsten
connector
interface board
Omnetics
connector
dental acrylic
skull bone
cortex
(motor area)
1mm
50 µm
250 µm
250 µm
polyimide-coated
tungsten
connector
interface board
Model-based analysis of cortical recording with silicon
Microelectrodes (Michael A. Moffitt, Cameron C. McIntyre*)
Distant vs. Local Signal Sources
An extracellular action potential from a local neuron is not
likely to register the same waveform on multiple wires
9. F
PCA Analysis
-80
-40
0
40
Voltage(µV) Correlated segments only
Uncorrelated segments
only
E
Time (ms)
0 3
Mean Spike Analysis
0 2000 4000 6000
4000
8000
Frequency (Hz)
Threshold Detected
Uncorrelated
Only Correlated
D
Spectrum Analysis
F
PCA Analysis
Common-noise spikes often look neural !
…simple spike detection and sorting based on
individual electrodes is limited.
Can we use other electrodes to help?
Individual Electrode Analysis Limitations
10. Inter-electrode Correlation
(IEC) Algorithm
• Design Goals:
– Reduce false-positive spike detection by
identifying and removing highly correlated
spiking events
– Be conducive to quantitative assessment of
neural interface recording performance (no
altering of signals at their source)
– Simple and objective
11. 0 5
Time (s)
200
-200
Voltage(µV)
E1
E2
E3
E8
Amplification/filtering, A/D conversion, digital filtering
analyzed
electrode
Experimental Overview: Signal Processing
Spike
Detection
*
Correlation
Algorithm
Mean Spike
(T-PCA:
Threshold+PCA)
Mean Spike
(T:Threshold
Only)
Mean Spike
(Corr)
***
Mean Spike
(Corr-PCA)
*
*
Principal
Component
Analysis
Principal
Component
Analysis
Omnetics
connector
dental acrylic
skull bone
cortex
(motor area)
1mm
50 µm
250 µm
250 µm
polyimide-coated
tungsten
connector
interface board
Threshold-based Spike
Detection Block
*
Voltage(µV)
40
-80
0
***
0 0.75 1.5 2.25 3
-100
0
Peak to Peak
Amplitude
Depolarization
Phase
Repolarization
Phase
Voltage(µV)
60
time (ms)
All Valid Spikes
Mean Spike
Mean Spike CalculationDetails of Correlation Algorithm
Time (ms)
0 31.5
40
-50
0
0 31.5
Voltage(µV)
Spike with low
inter-electrode
correlation
Spike with high
inter-electrode
correlation
KEEP! DISCARD!
** R<0.75 R>0.75
12. -100
60
0
uV
2.251.50.750 3
ms
Rater Selected
unsorted
80
90
100
110
120
130
140
150
410 420 430 440 450 460 470 480
Depolarization Phase (uSec)
PeaktoPeakAmplitude(uV)
E1
E4
U
U
RS
RS
Rationale for Performance Evaluation
Increasing neural content
Assumption:
Mean-spikes generated by true
neural spikes will be sharper
and larger in amplitude
Unsorted= includes all spikes that crossed threshold
Rater Selected= manually selected by experienced user
13. Data Set Details
• Neural recordings from our previous collagenase-aided
electrode insertion study*
• Chronic microwire implants (8ch) in rat motor cortex
• 12kHz sampling (300-5kHz filtering)
• 1 data set = 5 minutes of recording obtained during a
daily recording session (10 total sets)
• Subjects were awake during recording
* Paralikar, K.J. & Clement, R.S. Collagenase-Aided Intracortical Microelectrode Array Insertion: Effects
on Insertion Force and Recording Performance. IEEE Transactions on Biomedical Engineering. Vol 55,
No. 9, September 2008. (Available Online)
16. NumberofEvents
Results: Event Detection Rates
• Use of inter-electrode correlation significantly reduces event counts.
• This could lessen down the load on downstream processes.
** **
**p<0.001
17. Initial Performance Conclusions
• Removing highly correlated spike events
improves the mean-spike shape (more
neural-like)
• Inter-electrode correlation is a simple
technique to reduce false positive spikes
18. Comparative Study*
• Study Goals:
– Compare results from:
• Simple-threshold
• Differential referencing
• Virtual reference (common average subtraction)
• Inter-electrode correlation
– Explore performance under different degrees
of common noise
*Paralikar K, Rao C, Clement R. New approaches to eliminating non-neuronal
artifacts in recordings from intra-cortical microelectrode arrays: inter-electrode
correlation and virtual referencing. Journal of Neuroscience Methods
(Submitted)
19. Data Set SelectionData Selection
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Data Set
PercentageDifferenceor
CorrelationCoefficient
RMS difference b/w electrode and virtual reference
Correlation Coefficient
Low Common Noise Medium Common Noise
High Common Noise
• Each data set was composed of 5-minutes of neural recordings from motor
cortex of chronically implanted rats
20. 0 5
Time (s)
200
-200
Voltage(µV)
E1
E2
E3
E8
Omnetic
s
connect
or
dental
acrylic
skull bone
cortex
(motor area)
1mm
50
µm
250 µm
250 µm
polyimide-coated
tungsten
connector
interface
board
Amplification/filtering, A/D conversion, digital filtering
Ensemble Average
“Virtual Reference”
differential
reference
analyzed
electrode
Experimental Overview: Signal Processing
Spike
Detection
Spike
Detection
Spike
Detection
Correlation
Algorithm
Mean Spike
(Differential)
Mean Spike
(Threshold Only)
Mean Spike
(Correlation)
Mean Spike
(Virtual Reference)
Standard Thresholding Virtual Referencing
Inter-electrode CorrelationDifferential Reference
Signal Processing Paths:
21. Results: Mean-spike waveform samples
Mean Spikes
Correlation = 0.2
-120
-80
-40
0
40
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Low Common Noise Example
0 3
Voltage(µV)
Time (ms)
3
Correlation = 0.5
Medium Common Noise Example
0
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Correlation = 0.8
High Common Noise Example
0 3
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Mean Spikes
Correlation = 0.2
-120
-80
-40
0
40
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Low Common Noise Example
0 3
Voltage(µV)
Time (ms)
3
Correlation = 0.5
Medium Common Noise Example
0
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Correlation = 0.8
High Common Noise Example
0 3
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Correlation = 0.2
-120
-80
-40
0
40
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Low Common Noise Example
0 3
Voltage(µV)
Correlation = 0.2
-120
-80
-40
0
40
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Low Common Noise Example
0 3
Voltage(µV)
Time (ms)
3
Correlation = 0.5
Medium Common Noise Example
0
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
3
Correlation = 0.5
Medium Common Noise Example
0
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Correlation = 0.8
High Common Noise Example
0 3
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Correlation = 0.8
High Common Noise Example
0 3
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Threshold detected
Virtual Referencing
Differential Recording
Inter-electrode Correlation
Inter-electrode correlation method (- - -) consistently produced the
most neural appearing mean-spike waveforms under all common
noise conditions
22. Results: Mean-spike feature analysis
Medium Common-noiseLow Common-noise High Common-noise
Depolarization Phase Duration (µs)
Mean-spikePeak-to-Peak(µV)
24. Overall Conclusions
• Inter-electrode correlation (IEC) is noise resilient
• IEC is a pre-processing step: Can be used with threshold
detection and subsequent sorting methods
• May help reduce system requirements for downstream
operations (lower false positives)
• IEC may be useful in chronic neural interface
benchmarking studies because spike amplitude and
orientation are preserved for each electrode site
25. Future Considerations
• Correlation threshold for accept/reject can be
manipulated according to experimental need
• May incorporate spatial weighting matrix for
more closely spaced electrode sites
• Detect periods of high correlation and utilize
more intensive means to extract spikes
26. Acknowledgments
Penn State
• Timothy Gilmour (EE)
• Roger Gaumond
(emeritus)
• Jon Lawrence
External Consultants
• Steve Bierer (Univ.
Washington)
• Byron Olson
Partial Funding Provided By
• Penn State Department of Bioengineering
• National Institute of Deafness and
Communications Disorders (R21DC007227)