AN ANALOG VLSI IMPLEMENTATION OF A MULTI-SCALE SPIKE DETECTIONALGORITHM FOR EXTRACELLULAR NEURAL RECORDINGS

Christy L. Rogers1, John G. Harris1, Jose C. Principe1, and Justin C. Sanchez 2

1Department of Electrical and Computer Engineering, 2Department of Pediatrics Division of NeurologyUniversity of Florida, Gainesville, FL 32611, USA

[christy, harris, principe, justin]@cnel.ufl.edu

ABSTRACT

This paper discusses a multi-scale neural spike detec-tion algorithm for a low-power analog circuit implementa-tion. The key idea is to implement wavelet decompositionand improve spike detection by independently controllingthresholds for each scale. Each thresholded scale is thencombined to provide a single output indicating a spike oc-currence. This spike detection algorithm shows promisingresults towards a robust, compact, and unsupervised lowpower analog spike detection circuit. A low power front-end spike detection circuit can be added to a neural ampli-fier and dramatically reduce the required data bandwidth forBMI applications.

1. INTRODUCTION

Brain-machine interfaces (BMI) [1] require ultra-low powerneural instrumentation electronics, which may include spikedetection circuitry for chronical implantation. No externalwires should pass through the skin due to the risk of infec-tion. Low power is necessary due to the difficulty of charg-ing or changing implanted batteries. Implantation at thisstage of technology also requires a means of data reduction,which is normally implemented as a robust spike detectionmethod. Robustness is necessary because of varying noisesources, SNR fluctuations, and DC drift.

BMI systems place strong constraints on the wirelesstransmission because hundreds of channels are currentlyrecorded with the desire to reach thousands in the future.Transmitting raw voltages from 100 channels at a 25kHzsampling rate and 8 bits of resolution will generate datarates around 20Mbps, twice the current standard wirelesschannel transmission bandwidth. Because spikes are asparse portion of neural recordings only transmitting in-formation about the spikes would significantly reduce therequired transmission bandwidth. After the spikes are de-tected there are three main degrees of bandwidth reduction,only transmit a portion of the waveform around the spike,only send the features needed for spike sorting, or the ul-

timate bandwidth reduction is to only transmit the spiketimes.

Spike detection is a classical problem in neuroscience,with many proposed algorithms in the literature [2]. Pop-ular spike detection methods include amplitude threshold-ing, wavelets, matched filters, and template matching. Cur-rently, there is no consensus in the community as to thebest approach to spike detection, particularly for robust,unsupervised, and computationally simple methods. Eachof the proposed detection techniques has shortcomings forimplanted applications. The simplest method, amplitudethresholding, quickly begins to fail as SNR drops and isnot robust to DC drift. While, traditional wavelets havebeen shown to perform well in real-time analysis [3] currentwavelet circuits consume too much power for implantation.Template matching or matched filtering is the most accu-rate spike detection method, but they both require intensivecomputation and stable templates [2].

Previously, our lab developed an onset spike detector[4] in ultra low-power analog VLSI hardware, which is thebackbone of the proposed work. For implanted circuitry ananalog implementation is advantageous over a digital imple-mentation because it has a much lower power consumptionand it is more compact in size because of its clever design.The improvement of the onset detector is based on a multi-resolution decomposition of the spikes, where independentthresholds are applied to each scale and help to better dis-criminate the spike shape. Such an implementation wouldbe robust in detecting spikes recorded both near and far fromthe neuron cell body where the distance affects the ampli-tude and time constant of the spike. In ideal cases wherethe pdfs of the signals are Gaussian and known the Bayes’detector eqn. (1) provides the optimal threshold assumingthe costs of false alarms and missed detection are weightedequally and the variance of the two pdfs are equal. In thisequation σ2 represents the variance, µ the mean, and Pi isthe probability of noise (P0) or the probability of spike plusnoise (P1).

Figure 1: Onset Detector Circuit Diagram

λ = σ20

ln(P0P1

)µ1 − µ0

+µ1 + µ0

2(1)

This paper provides a brief overview of the onset detec-tor for spike detection with chip results in Section 2. Then,its multi-scale extension is discussed in Section 3 and Mat-lab simulations from recorded neural data are used to evalu-ate the performance of the multi-scale algorithm in Section4.

2. ONSET SPIKE DETECTOR CHIP

The basic operating principle for the onset spike detectoris inspired by the auditory onset detection scheme of Smith[5]. The difference of two low-pass filters is used to enhancethe spike and stabilize the baseline. One filter has a highercutoff frequency to remove high frequency noise and theother has a lower cutoff frequency to create a local average.When the difference between the signal and the local aver-age rises above a threshold, a spike is detected. This methodis robust to changes in the noise level as well as DC offsets,both of which are common for long term neural recordings.The basic circuit blocks are shown in Fig. 1.

An operational transconductance amplifier (OTA) isconfigured as a follower integrator for the first-order low-pass filters. The OTAs are run in the subthreshold regionto reduce power [6][7]. The τ bias voltages are set off chipto enable adjustment of the cutoff frequencies after fabrica-tion. Neural spikes can vary in width from 0.3ms to 3msdepending on the species and brain area so the cut-off fre-quencies are set to remove all of the noise outside the spikefrequency ranges for the particular application. The thresh-old is set with Vbias but would require an automatic methodfor unsupervised operation. Cadence SpectreS simulationsshowed the circuit consumes an average of 1µW of power.The onset spike detector chip was fabricated using AMI0.5µm CMOS technology. The chip was 1.5mm× 1.5mmwith 253µm × 223µm of circuit area.

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Figure 2: Onset Detector Chip Results. Top waveform isthe amplified input to the chip. The Bottom Waveform isthe chip’s output.

Bionic’s 128-Channel Neural Signal Simulator was usedas an initial test input for the spike detector chip. The neu-ral simulator outputs a repeated 11 second pattern of spikesfrom three different action potentials with amplitudes of100µV - 150µV and a width of 1ms. The interspike in-terval is 1s for 10s and then the interspike interval reducesto 10ms for one second of burst firing. The UF bioampli-fier [8], with a gain of 100, was used to amplify the neuralsimulator’s output, as would be used in a BMI system. Thespike detector was able to detect 99% of the spikes withoutany false alarm. This is approximately one missed detec-tion per second during peak neural firings. By decreasingthe threshold slightly the detector reached a 100% detec-tion rate but a few spikes were detected twice creating falsealarms. Blinding the detector for a short period after ev-ery detection would eliminate this problem, but would alsokeep the detector from detecting two spikes closer than theblinding period. An example of the spike detector’s outputfrom the amplified neural simulator signal is shown in Fig.2. The same chip was tested with in vivo neural recordingsbut we are still characterizing its performance.

3. MULTI-SCALE SPIKE DETECTION

The multi-scale spike detector extends the previous methodof onset detection to multiple scales to allow the detectionof spikes with a wider range of widths without sacrificingperformance. This is achieved with a multi-scale gammafilter [9] (cascade of low-pass filters) as shown in Fig 3.

To achieve a wide-range of cut-off frequencies, a resis-tive line is connected along the bias controls of each low-pass filter. With the OTAs operated in the subthreshold re-gion, this linear voltage drop across the resistive line pro-vides an exponential change in the bias currents, which inturn proportionally varies the cutoff frequencies. This al-lows each filter to be constant Q, meaning the ratio betweenthe center frequency of the filter and the spectrum widthremains constant, which provides localization in both the

Figure 3: Multi-scale Gamma filter circuit

time and frequency domains. This scheme was shown to bea wavelet by Burrus, Gopinath, and Guo [10]. The transferfunction of the kth stage is given by

Hk(s) =1

akτs + 1(2)

where a is a preset attenuation factor.Currently, a five-tap gamma filter is used. The differ-

ence of each set of neighboring taps, Xk − Xk−1, formbandpass filters and are thresholded to determine the pres-ence of a spike at each scale. The outputs of all the scalesare ORed together after appropriate compensation for theirvarying delays. If this spike detector was to be used in an ap-plication requiring spike sorting, each scale’s output couldbe transmitted to send the spike width feature and a simplepeak detector circuit [11] could be implemented to send theamplitude feature of the spike.

Setting thresholds by hand is cumbersome for a largenumber of channels. For the multi-scale algorithm eachscale’s threshold would have to be set independently so thismay prohibit finding the optimal threshold levels for the al-gorithm to work its best. To solve this problem the basicBayes’ threshold eqn. (1) was extended to determine howthe optimal threshold should vary with scale. Thus, onlyone threshold needs to be set by the user and the others areautomatically set from it.

4. RESULTS

4.1. Data

The algorithm was tested on neural recordings from maleSprague-Dauley rats chronically implanted with 50µm mi-crowire electrode arrays in layer V of the forelimb region ofthe primary motor cortex. The data was sampled at 25KHzand bandpass filtered between 0.5 and 12kHz using hard-ware from Tucker-Davis Technologies. Action potentialwidths ranged from 0.4ms - 1.2ms with amplitudes as highas 137µV . High SNR recordings were chosen to increasethe confidence of the ground truth spikes times.

To make this high SNR recording more similar to thetypical recordings, white Gaussian noise was added. Aslowly varying 0.1Hz, 10µV amplitude sinusoid was alsoadded to the signal to simulate DC offsets. Fig. 4 (a) shows

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Figure 4: (a) original waveform (b) 0dB SNR waveformwith offset. Column two is zoomed in from column one.

the original neural data waveform and (b) shows the 0dBSNR waveform with an offset. SNR was calculated as theaverage spike magnitude divided by the average noise mag-nitude.

4.2. Matlab Simulations

Receiver operating characteristic (ROC) curves are typi-cally used to quantify the performance of detection algo-rithms [12]. There is always a trade-off between the opti-mal detection of all the spikes and the erroneous detectionof noise as a spike. This detection problem also requiresspike time estimation. A detection was considered correctif it occurred within 500µs of the actual spike time. Theratio of correct detections to incorrect detections can be setto the desired operating point on the ROC by choosing thecorresponding threshold level.

The multi-scale detection method was compared to thesingle-scale method at 0dB SNR over 120s of neural datawith the results shown in Fig. 5. For comparison purposesthe methods were examined at their 90% correct detectionoperating point. The multi-scale method only had 15 falsealarms per second, while the onset detector had 112. Thus,the multi-scale detector outperformed the single-scale de-tector by over 15dB in terms of false alarms for a 90% cor-rect detection rate. Because spikes are sparse in neural datathe probability of a false alarm needs to be a fraction of apercent not to swamp the number of correct detections.

The data used has an average spiking rate of 63Hz soduring one second of data at 90% correct detections thereshould be about 57 correct detections out of 63. The multi-scale algorithm has a similar performance boost over var-ious SNRs down to -5dB but below this SNR level noneof the methods tested performed well enough to be used ina BMI system. For larger SNR values the multi-scale al-

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Figure 5: ROC Curves, 0dB SNR

gorithm continued to outperform the single-scale method.An analysis of the false alarms detected by the multi-scalemethod showed that almost one-third of the false alarmswere from spikes whose negative side was larger than thethreshold but whose positive side was smaller than thethreshold. This means that ground truths, such as our own,done by examining events that surpass a single thresholdmay be misleading.

5. CONCLUSION

Matlab validations with real neural recordings were usedto test the multi-scale spike detection algorithm prior to itsimplementation in analog VLSI. The results showed it im-proved upon the single-scale detector’s performance, andwill therefore be an excellent compromise between power,transmission bandwidth, area, and robustness when it is im-plemented in a circuit. The multi-scale circuit is expectedto be ultra-low power (∼ 10µW ). Though the results ofcomputationally intensive methods such as matched filter-ing were not examined, they are sure to provide better re-sults given enough information is known about the signal.However, even if enough information about the signals wereknown, their power consumption and required supervisionto adjust parameters as spike shapes and noise change overtime prohibits implantation.

6. ACKNOWLEDGMENTS

This material is based on work supported under a NSFGraduate Research Fellowship. Any opinions, finding, con-clusions, or recommendations expressed in this publica-tion are those of the authors and do not necessarily reflectthe views of the NSF. This research is also supported byDARPA sponsor grant #N66001-02-C-8022.

7. REFERENCES

[1] J. Carmena, M. Lebedev, R. Crist, J. O’Doherty,D. Santucci, D. Dimitrov, P. Patil, C. Henriquez, andM. Nicolelis, “Learning to control a brain-machine in-terface for reaching and grasping by primates,” PLoSBiology, vol. 1, no. 2, pp. 193–208, Nov. 2003.

[2] M. Lewicki, “A review of methods for spike sorting:the detection and classification of neural action poten-tials,” Computation and Neural Systems, vol. 9, pp.R53–R78, 1998.

[3] Z. Nenadic and J. Burdick, “Spike detection usingthe continuous wavelet transform,” IEEE Transactionson Biomedical Engineering, vol. 52, no. 1, pp. 74–87,Jan. 2005.

[4] C. L. Rogers and J. G. Harris, “A low-power analogspike detector for extracellular neural recordings,” inInt’l. Conf. IEEE Electronics, Circuits and Systems,Tel-Aviv, Israel, Dec. 2004.

[5] L. Smith, “Using an onset-based representation forsound segmentation,” in Int’l. Conf. on Neural Net-works and their Applications, Marseilles, France, Dec.1995.

[6] C. Mead, Analog VLSI and Neural Systems, Addison-Wesley, 1989.

[7] Liu, Kramer, Indiveri, Delbruck, and Douglas, AnalogVLSI: Circuits and Principles, MIT Press, 2002.

[8] D. Chen, J. G. Harris, and J. C. Principe, “A bio-amplifier with pulse output,” in Int’l. Conf. IEEE En-gineering in Medicine and Biology Society, San Fran-cisco,California, 2004.

[9] J. G. Harris, J. Juan, and J. C. Princpe, “Analog hard-ware implementation of continous-time adaptive fil-ter strucutres,” Analog Integrated Circuits and SignalProcessing, vol. 18, pp. 209–227, Feb. 1999.

[10] C. Burrus, R. Gopinath, and H. Guo, Introduciton toWavelets and Wavelet Transforms A Primer, Prentice-Hall, 1998.

[11] T. Horiuchi, T. Swindell, D. Sander, and P. Abshier, “Alow-power cmos neural amplifier with amplitude mea-surements for spike sorting,” in International Sympo-sium on Circuits and Systems, May 2004, pp. IV 29–32.

[12] R. Hippenstiel, Detection Theory Applications andDigital Signal Processing, CRC Press, 2002.