Method and System for Detecting and Classifying Facial Muscle Movements

• US 20070179396A1
• Filed: 09/12/2006
• Published: 08/02/2007
• Est. Priority Date: 09/12/2005
• Status: Abandoned Application

• Current Assignee: Emotiv Systems Pty., Ltd.
• Original Assignee: Emotiv Systems Pty., Ltd.
• Inventors: Thie, Johnson, Delic, Emir, Do, Nam, Pham, Hai, King, William, Le, Tan Thi Thai, Lo, Vivian, Doyle, Briony, Della Torre, Marco
• Application Number:
  US11/531,117
• Publication Number:
  US 20070179396A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of detecting and classifying facial muscle movements, comprising the steps of: receiving bio-signals from at least one bio-signal detector; and applying at least one facial muscle movement-detection algorithm to a portion of the bio-signals affected by a predefined type of facial muscle movement in order to detect facial muscle movements of that predefined type.

Claims

1. A method of detecting and classifying facial muscle movements, including the steps of:

a) receiving bio-signals from one or more than one bio-signal detector; and

b) applying one or more than one facial muscle movement-detection algorithm to a portion of the bio-signals affected by a predefined type of facial muscle movement in order to detect the facial muscle movements of the predefined type.

2. The method according to claim 1, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes comparing the bio-signal portion to a signature defining one or more than one distinctive signal characteristics of the predefined facial muscle movement type.

3. The method according to claim 2, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes directly comparing bio-signals from one or more than one predetermined bio-signal detectors to that signature.

4. The method according to claim 2, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes:

a) projecting bio-signals from the plurality of bio-signal detectors on one or more than one predetermined component vectors; and

b) comparing the projection of the bio-signals onto one or more than one component vectors to that signature.

5. The method according to claim 4, further including applying a desired transform to the projected bio-signal after the projection of the bio-signals from the plurality of detectors on one or more than one component vectors, and before the projected bio-signal is compared to that signature.

6. The method according to claim 4, wherein the predetermined component vectors are determined by applying a first component analysis to historically collected bio-signals generated during facial muscle movement types of the type corresponding to that signature.

7. The method according to claim 6, wherein the first component analysis applied to the historically collected bio-signals is independent component analysis (ICA).

8. The method according to claim 6, wherein the first component analysis applied to the historically collected bio-signals is principal component analysis (PCA).

9. The method according to claim 4, wherein the one or more than one component vectors are updated during facial muscle movement-detection and classification.

10. The method according to claim 2, further including updating the signature during the course of facial muscle movement-detection and classification.

11. The method according to claim 10, wherein the signature is updated by changing thresholds forming at least part of the distinctive signal characteristics of the signature.

12. The method according to claim 2, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes:

a) applying a desired transform to the bio-signals; and

b) comparing the results of the desired transform to that signature.

13. The method according to claims 12, wherein the transform is one or more than one transform selected from the group consisting of a Fourier transform and a wavelet transform.

14. A method according to claim 4 further including:

a) applying a second component analysis to the detected bio-signals; and

b) using the results of the second component analysis to update the one or more than one predetermined component vectors during bio-signal detection.

15. The method according to claim 14, wherein the second component analysis is principal component analysis (PCA).

16. The method according to claim 1, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes separating the bio-signals resulting from the predefined type of facial muscle movement from one or more than one sources of noise in the bio-signals.

17. The method according to claim 16, wherein the sources of noise comprise one or more than one source selected from the group consisting of electromagnetic interference (EMI), and bio-signals not resulting from the predefined type of facial muscle movement.

18. The method according to claim 2, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes comparing the sum or difference of bio-signals from one or more pairs of bio-signal detectors to that signature.

19. The method of claim 18, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals further includes comparing bio-signals from each of the one or more pairs of bio-signal detectors to that signature.

20. The method of claim 19, wherein the comparing step includes:

tracking a derivative of one or more than one of the bio-signals from each of the one or more pairs of bio-signal detectors and the sum or difference of bio-signals from the one or more pairs of bio-signal detectors.

21. The method of claim 20, wherein the comparing step further includes:

comparing one or both of gradient and amplitude for one or more than one of the bio-signals from each of the one or more pairs of bio-signal detectors and the sum or difference of bio-signals from the one or more pairs of bio-signal detectors; and

determining when one or both of the gradient and amplitude respectively exceeds predetermined gradient and amplitude thresholds.

22. The method of either of claims 20 or 21, wherein the comparing step further includes:

computing the correlation between bio-signals from each of the one or more pairs of bio-signal detectors; and

determining when the correlation exceeds a predetermined correlation threshold.

23. The method according to claim 2, wherein the step of applying one or more than one facial muscle movement-detection algorithm to the bio-signals includes comparing the power of bio-signals from one or more predetermined bio-signal detector to that signature.

24. The method according to claim 23, wherein the comparing step includes summing the power of bio-signals from one or more pairs of bio-signal detectors to that signature;

and

determining whether the sum exceeds a predetermined threshold indicative of a first facial muscle movement type.

25. The method according to claim 23, wherein the comparing step includes computing the ratio of the power of bio-signals from a first group of bio-signal detectors to the power of bio-signals from a second group of bio-signal detectors;

and

determining whether the ratio exceeds a predetermined threshold indicative of a second facial muscle movement type.

26. The method according to claim 1, wherein the bio-signals include electroencephalograph (EEG) signals.

27. The method according to claim 1, further including generating an output signal representative of the detected facial muscle movement type.

28. An apparatus for detecting and classifying facial muscle movements, including:

a processor and associated memory device for causing the processor to carry out a method according to claim 1.

29. A computer program product, tangibly stored on machine readable medium, the product comprising instructions operable to cause a processor to carry out a method according to claim 1.