PULSE-BASED FEATURE EXTRACTION FOR NEURAL RECORDINGS

• US 20100081958A1
• Filed: 10/02/2007
• Published: 04/01/2010
• Est. Priority Date: 10/02/2006
• Status: Abandoned Application

• Current Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida)
• Original Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida)
• Inventors: She, Christy L.
• Application Number:
  US12/444,008
• Publication Number:
  US 20100081958A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/388 Nerve conduction study, e.g. detecting action potential of peripheral nerves
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: G06F 3/015 Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection

Abstract

A neural recording system (100) and method (400) for neural encoding is provided. The system can include an ultra-low power neural encoder (120) for compressing spikes within a neural signal (110) to produce a pulse train (130) and wirelessly transmitting the pulse train to a spike sorter (140). Features of the neural signal can be encoded such that the timing between pulses and the number of pulses conveys features of the spike. The neural encoder can include an Integrate and Fire (IF) neuron 230 that performs spike detection and encodes at least one spike (112) of the neural signal. A leakiness aspect (232) and an adaptive aspect (337) can be included with the IF circuit for combining aspects of spike detection and spike sorting for suppressing noise, keeping power consumption low, and improving signal resolution.

Claims

1. A neural acquisition system, comprising

a neural encoder that receives a neural signal and generates a temporal-based pulse-coded representation of the neural signal;

and

a spike sorter communicatively coupled to the neural encoder for receiving the temporal-based pulse coded representation, sorting spikes encoded in the temporal-based pulse coded representation, and identifying neurons generating the spikes,

wherein the neural encoder encodes features of the spikes as a timing between pulses and a number of pulses such that the timing and the number of pulses represent features of the spikes that are characteristic of neural signals produced by the neurons.

2. The neural acquisition system of claim 1, wherein the neural encoder generates a temporal-based pulse coded representation of spikes in the neural signal based on integrate-and-fire coding of the received neural signal.

3. The neural acquisition system of claim 1, wherein the neural encoder further comprises:

a processor for generating the temporal-based pulse coded representation of spikes in the neural signal;

a transmitter operatively coupled to the processor for wirelessly communicating the temporal-based pulse coded representation; and

a power source for powering the processor and the wireless module.

4. The neural acquisition system of claim 1, wherein the spike sorter further comprises:

a receiver for wirelessly receiving the temporal-based pulse coded representation from the neural encoder.

5. The neural acquisition system of claim 1, wherein the neural encoder and the spike sorter operate without explicit dependence on a discrete or fixed clock signal or other time based reference.

6. The neural acquisition system of claim 1, wherein the spike sorter operates directly on the timing of pulses for sorting spikes and avoids reconstruction of the neural signal.

7. The neural acquisition system of claim 1, wherein the spike sorter further comprises:

a classifier for synchronizing spike signatures, comparing the spike signatures to templates associated with neurons, and identifying a neuron producing a spike signature.

8. The neural acquisition system of claim 7, wherein the classifier

convolves a pulse train with a Gaussian function to produce an envelope;

and

compares the envelope to at least one template to identify a neuron.

9. A neural encoder suitable for use in bandwidth compression of a neural signal, the neural encoder comprising:

an Integrate and Fire (IF) circuit coupled to an electrode that captures a neural signal, wherein the IF circuit encodes at least one spike of the neural signal and generates a bi-phasic pulse train in accordance with features of the spike,

wherein the IF circuit introduces a timing between pulses of the pulse train and a number of pulses for encoding at least one feature of the waveform.

10. The neural encoder of claim 9, wherein the IF circuit includes a leaky integrator that increases a robustness to noise and allows for synchronizing spike signatures.

11. The neural encoder of claim 9, wherein the IF circuit decreases a period of the pulses for high-amplitude spikes, and increases the period of the pulses for low-amplitude spikes for bandwidth compression.

12. The neural encoder of claim 9, wherein the IF circuit includes a spike detector for identifying spikes prior to the encoding.

13. The neural encoder of claim 9, wherein the IF circuit includes an adaptive component that adjusts the timing and number of pulses for bandwidth compression.

14. The neural encoder of claim 9, wherein the IF circuit models an area of the waveform as a feature to determine the timing between pulses and the number of pulses for representing a spike.

15. The neural encoder of claim 9, wherein the IF circuit models an amplitude of the waveform as a feature to determine the timing between pulses and the number of pulses for representing a spike.

16. The neural encoder of claim 9, wherein the IF circuit includes at least one user setting for adjusting a bandwidth compression of the pulse train.

17. The neural encoder of claim 9, further comprising a bank of Integrate and Fire (IF) neuron models tuned to different frequency bands to span a range for temporal-based pulse coding of the neural signal.

18. An Integrate and Fire (IF) circuit suitable for use in bandwidth compression of a neural signal, comprising:

a leaky integrator to integrate a neural signal to produce an integrated signal;

a pulse generator to produce a bi-phasic pulse train based on the integrated signal,

wherein features of the neural signal are encoded as a timing between pulses of the pulse train and a number of pulses such that the timing between pulses and the number of pulses conveys features of the spike.

19. The IF circuit of claim 18, further comprising an amplifier operatively coupled to the capacitor and providing input to the capacitor for increasing a gain of the neural signal prior to the integrating.

20. The IF circuit of claim 18, wherein the leaky integrator comprises:

a capacitor for building up a charge in accordance with a voltage of the neural signal; and

a resistor coupled in parallel with the capacitor that leaks of a portion of the charge, wherein the resistor provides a leakiness to the integrating by decreasing the charge on the capacitor over time.

21. The IF circuit of claim 18, wherein the pulse generator further comprises:

a bi-phasic comparator for generating a positive pulse output when the integrated signal exceeds a first threshold, and a negative pulse output when the integrated signal exceeds a second threshold; and

an OR gate coupled to the positive pulse output and negative pulse output for generating a bi-phasic output pulse train,

22. The IF circuit of claim 18, wherein the bi-phasic comparator further includes

a first comparator for generating a positive pulse output, wherein the first comparator includes a first adjustable threshold for setting a pulse rate based on a positive area of a spike;

and

a second comparator for generating a negative pulse output, wherein the second comparator includes a second adjustable threshold for setting a pulse rate based on a negative area of a spike;

23. The IF circuit of claim 18, further comprising:

a feedback unit coupling the output of the amplifier to the input of the amplifier for resetting the pulse generator to an initial state

24. The IF circuit of claim 23, wherein the feedback unit includes a delay element to adjust a timing between pulses of the pulse train for modeling a neural refractory period.

25. The IF circuit of claim 23, wherein the feedback unit includes an adaptive unit for monitoring a pulse rate and adjusting the timing between pulses and the number of pulses for bandwidth compression.

26. The IF circuit of claim 26, wherein the adaptive unit adjusts at least one of a resistance of the resistor, a capacitance of the capacitor, a threshold of the bi-phasic comparator, or a delay of a feedback for bandwidth compression of the pulse train.

27. A method for neural encoding, comprising:

integrating a neural signal to produce an integrated signal;

generating a pulse if a level of the integrated signal exceeds a threshold,

wherein characteristic features of the spikes are encoded as a timing between pulses and a number of pulses such that the timing and number of pulses represents features of the neural signal.

28. The method of claim 27, further comprising:

detecting a spike in the neural signal prior and encoding features of the spike to increase bandwidth compression of the neural signal.

29. The method of claim 27, further comprising:

introducing a leakiness to the integrating to produce a leaky integration.

30. The method of claim 27, further comprising:

wirelessly transmitting the pulse train asynchronously to a spike sorter, wherein the pulse train provides bandwidth compression of the spike.

31. The method of claim 27, further comprising:

enabling a power amplifier to transmit a pulse upon the integrating exceeding a threshold; and

keeping the power amplifier in power save mode otherwise.

32. The method of claim 27, further comprising:

sorting spikes encoded within the timing of the pulse train without reconstructing the neural signal.

33. The method of claim 27, wherein the generating further comprises:

comparing the integration to a positive threshold and generating a positive pulse if the integration exceeds the positive threshold; and

comparing the integration to a negative threshold and generating a negative pulse if the integration exceeds the negative threshold,

34. The method of claim 27, wherein the generating a pulse train further comprises:

adjusting a pulse rate and number of pulses in accordance with an area of a waveform of the spike.

35. The method of claim 27, wherein the generating a pulse train further comprises:

adjusting a pulse rate and number of pulses in accordance with an amplitude of a waveform of the spike.

36. The method of claim 27, wherein the generating a pulse train further comprises:

introducing a delay in a feedback of the pulse train for modeling a refractory period.

37. The method of claim 27, wherein the generating a pulse train further comprises:

adapting a threshold in accordance with the timing between pulses and the number of pulses for modeling inhibition and excitation.

38. The method of claim 27, wherein the generating a pulse train suppresses noise on the spike.