Methods and systems for denoising a neural recording signal

1. A method of denoising a neural recording signal, said method comprising:

applying a stimulus pulse to neural tissue to generate a neural recording signal;

correlating said neural recording signal with one or more basis functions derived using principal component analysis to produce one or more weights; and

multiplying said weights with said basis functions to obtain a denoised neural recording signal.

2. The method of claim 1 , wherein said one or more basis functions comprises at least two basis functions and less than or equal to seven basis functions.

3. The method of claim 1 , further comprising:

collecting a plurality of neural recording signals m 1 through m n into a measurement matrix M; and

using said principal component analysis to derive a matrix V full that comprises a complete set of basis functions that account entirely for said measurement matrix M.

4. The method of claim 3 , further comprising selecting said one or more basis functions that are correlated with said neural recording signal from said complete set of basis functions within said matrix V full by analyzing at least one or more of a percent of unaccounted variance of said neural recording signals in said measurement matrix M and a difference of standard deviations of an error in a beginning portion versus an end portion of said neural recording signals in said measurement matrix M.

5. The method of claim 1 , wherein said step of multiplying said weights with said basis functions comprises use of the following equation:

m denoised =T m=T s+T n;

wherein m represents said neural recording signal, s represents a signal that comprises at least one or more of a neural response signal and an artifact signal, n represents an uncorrelated noise signal, T represents a denoising matrix equal to a first matrix comprising said one or more basis functions multiplied by a transpose of said first matrix, and m denoised represents said denoised neural recording signal.

6. The method of claim 5 , further comprising describing said noise signal with a covariance matrix CD n =E[n n′]=T E[n n′]T′=T C n T′.

7. The method of claim 1 , further comprising:

designating said neural recording signal as including an evoked neural response if an amplitude of said denoised neural recording signal exceeds a confidence interval.

8. The method of claim 1 , further comprising determining a minimum amplitude of said stimulus pulse that evokes a neural response signal.

9. The method of claim 1 , wherein said neural recording signal corresponds to an auditory nerve.

10. A system for denoising a neural recording signal, said system comprising:

a neural stimulator configured to generate a neural recording signal by stimulating neural tissue with a stimulus pulse; and

at least one device in communication with said neural stimulator and configured to

correlate said neural recording signal with one or more basis functions derived using principal component analysis to produce one or more weights; and

multiply said weights with said basis functions to obtain a denoised neural recording signal.

11. The system of claim 10 , wherein said one or more basis functions comprises at least two basis functions and less than or equal to seven basis functions.

12. The system of claim 10 , wherein said at least one device is further configured to:

designate said neural recording signal as including an evoked neural response if an amplitude of said denoised neural recording signal exceeds a confidence interval.

13. The system of claim 10 , wherein said neural stimulator comprises an implantable cochlear stimulator.

14. The system of claim 10 , wherein said neural stimulator comprises at least one or more of a spinal cord stimulator, a deep brain stimulator, and a microstimulator.

15. A system for denoising a neural response signal, said system comprising:

means for applying a stimulus pulse to neural tissue to generate a neural recording signal;

means for correlating said neural recording signal with one or more basis functions derived using principal component analysis to produce one or more weights; and

means for multiplying said weights with said basis functions to obtain a denoised neural recording signal.

16. The system of claim 15 , wherein said one or more basis functions comprises at least two basis functions and less than or equal to seven basis functions.

17. The system of claim 15 , further comprising:

means for designating said neural recording signal as including an evoked neural response if an amplitude of said denoised neural recording signal exceeds a confidence interval.

18. The system of claim 15 , further comprising means for determining a minimum amplitude of said stimulus pulse that evokes said neural response signal.