Inter-Electrode Correlation

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Inter-electrode correlation (IEC) algorithm for reducing common-noise artifacts in multichannel neural recordings (IEEE/EMBS 2008)

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  • Motivation: large amount of data…many electrodes many days (6 weeks)…need for objective technique.
  • A~150um
  • (DON:T SAY CORRELATED, YET) Often times accentuated in chronic, awake subjects.
  • Problem with simple thresholding…often times the common signals can be spike-like!
  • Very quick! Do not read bullets! Real data, awake subjects
  • Be quick…just say compared with other techniques designed to reduce common noise interference.
  • Inter-Electrode Correlation

    1. 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. 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. 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. 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
    5. 5. Classic Signal Processing Chain Spike detection threshold Filtered signal PCA Clusters Template Sorting Detect Spikes Classify/ sort Analyze Spiketrains Detected spikes Classified Spikes Figures from: www.plexoninc.com
    6. 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)
    7. 7. -200 0 200 1300 65 Time (ms) -60 -80 40 40 30 Time (ms) Voltage(µV) Voltage(µV)Voltage(µV) Two Types of Suprathreshold Spiking Events E1 E2 E3 E4 Spike on E3 Segments on other electrodes
    8. 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. 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. 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. 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. 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. 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)
    14. 14. 2.25 30 0.75 1.5 ms 2.251.50.750 -70 40 0 uV Thresholding and Correlation Thresholding Alone Results: Mean-Spike Waveform Samples -600 -400 -200 0 200 400 600 -600 -400 -200 0 200 400 600 Center 10,10 Radius 200 -600 -400 -200 0 200 400 600 -600 -400 -200 0 200 400 600 Center 10, 10 Radius 200 PCA Analysis Threshold Alone Threshold w/ Corr 2.251.50.750 -70 40 0 ms uV
    15. 15. Duration(µs) Duration(µs)Voltage(µV) Depolarization Phase Repolarization Phase Peak-Peak Amplitude 0 0.75 1.5 2.25 3 -100 0 Peak-Peak Amplitude Depolarization Phase Repolarization Phase Voltage(µV) 60 time (ms) N = 10 electrodes * p<0.01 T: Thresholded Only T_PCA: Thresholded w/ PCA Corr: Threshold + Correlation Corr_PCA: Thresh + Correl + PCA Results: Mean-Spike Feature Analysis * * * *
    16. 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. 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. 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. 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. 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. 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. 22. Results: Mean-spike feature analysis Medium Common-noiseLow Common-noise High Common-noise Depolarization Phase Duration (µs) Mean-spikePeak-to-Peak(µV)
    23. 23. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Low Medium High EventCounts(normalized) Relative Common-noise Level Thres VirtualRef Differential Correlation Results: Event Detection Rates Low Common Noise Thres-Detected Virtual-Ref Differential Inter-Elect Cor Dep Phase (uSec) 461.3±18 475.6±19.8** 796.8±212.4** 465.8±17** Rep Phase (uSec) 1401.7±268.6 1304±183.5 883.6±542.9** 1254.2±131.7 Pk to Pk Amp (uV) 110.6±19.8 91.5±17.7** 121.2±17.2 100±12.6 SpikeSegments 11055±2432 11211±1893 9957±1501 8791±1914** Med. Common Noise Dep Phase (uSec) 641.9±197.4 515±1467.1 575±72.8 497.6±49.2 Rep Phase (uSec) 1357.5±410.5 1467.1±63.4 1355.9±205.3 1359.2±79.6 Pk to Pk Amp (uV) 98.2±14.1 70.4±12.2** 87.6±15.5 93±10 SpikeSegments 7713±1982 9513±2760** 8088±1979 4407±2688** High Common Noise Dep Phase (uSec) 760.3±214.3 514.3±85.9 615.6±124.7 544.6±81.9 Rep Phase (uSec) 1083±662.4 1628.8±183.5 1600.1±222.8 1308.4±328.5 Pk to Pk Amp (uV) 73.1±16.5 44.83±16.9 48.3±10.8** 56.6±12.1 SpikeSegments 4735±2527 5823±3636 4598±3018 1206±1859** Summary Table
    24. 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. 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. 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)
    27. 27. Thank you! • Questions?
    28. 28. Receiver-Operator Characteristic Curve Analysis 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 A Low Common-Noise 0.2 0.4 0.6 0.8 1 A~0.82 Medium Common-Noise 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 B ~0.75 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 C High Common-Noise ~0.82 False Positive Rate TruePositiveRate 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 A Low Common-Noise 0.2 0.4 0.6 0.8 1 A~0.82 Medium Common-Noise 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 B ~0.75 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 C High Common-Noise ~0.82 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 A Low Common-Noise 0.2 0.4 0.6 0.8 1 A~0.82 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 A Low Common-Noise 0.2 0.4 0.6 0.8 1 A~0.82 Medium Common-Noise 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 B ~0.75 Medium Common-Noise 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 B ~0.75 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 B ~0.75 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 C High Common-Noise ~0.82 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 C High Common-Noise ~0.82 False Positive Rate TruePositiveRate

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