Single trial detection in encephalography

• US 7,835,787 B2
• Filed: 10/15/2004
• Issued: 11/16/2010
• Est. Priority Date: 05/03/2002
• Status: Active Grant

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Inventors: Sajda, Paul, Parra, Lucas Cristobal
• Primary Examiner(s): Nasser; Robert L
• Assistant Examiner(s): D'ANGELO, MICHAEL J
• Application Number:
  US10/966,290
• Publication Number:
  US 20090326404A1
• Time in Patent Office: 2,223 Days
• Field of Search: 600/300, 600544-547
• US Class Current: 600/544
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/375 using biofeedback
• CPC Class Codes: G06F 2218/22 Source localisation; Inverse modelling

Abstract

An EEG cap (8) having 64 or 128 electrodes (10) is placed on the head of the subject (11) who is viewing CRT monitor (14). The signals on each channel are amplified by amplifier (17) and sent to an analog-to-digital converter (20). PC (23) captures and records the amplified signals and the signals are processed by signal processing PC (26) performing linear signal processing. The resulting signal is sent back to a feedback/display PC (29) having monitor (14).

Claims

1. A method for interpreting brain activity comprising the steps of:

attaching a high-density array of brain activity sensors to a subject;

prompting said subject with an external stimulus;

generating a response signal from each of said sensors at a predetermined point in time with reference to the occurrence of said prompting;

computing a respective spatial weighting coefficient corresponding to each of said sensors by applying a linear classifier to said predetermined number of response signals;

weighting each response signal in accordance with its corresponding weighting coefficient; and

linearly integrating the weighted response signals whereby a single output signal is generated, said output signal having a relatively high correlation to the brain activity of said subject in response to said external stimulus.

2. The method of claim 1 wherein said weighting coefficients are computed such that the weighted responses are maximally discriminating between first and second predetermined times corresponding to two stimuli where a maximum difference in brain activity is expected.

3. The method of claim 2 wherein the linear integration is a summation.

4. The method of claim 2 wherein said brain activity sensors are EEG sensors.

5. The method of claim 2 wherein said brain activity sensors are MEG sensors.

6. The method of claim 2 wherein said stimulus is visual.

7. The method of claim 1 further comprising the steps of:

identifying an expected scalp locality for discriminating components of the output signal relative to the external stimulus;

computing a sensor projection for each of said sensors;

using said sensor projections to identify the scalp locality for the actual discriminating components for said external stimulus; and

evaluating the validity of said output signal by determining whether the identified locality for the actual discriminating components is substantially the same as the expected locality.

8. A system for interpreting brain activity comprising:

a high-density array of brain activity sensors for application to the head of a subject; and

a computer for receiving signals from said sensors, said computer programmed to;

compute a respective spatial weighting coefficient corresponding to each of said sensors by applying a linear classifier to signals generated by said sensors in response to an external stimulus,

weight each of said signals in accordance with its corresponding weighting coefficient, and

linearly integrate the weighted response signals,

whereby a single output signal is generated, said output signal having a relatively high correlation to the brain activity of said subject in response to said external stimulus.

9. The system of claim 8 further comprising a feedback display.

10. The system of claim 9 wherein said feedback display provides said external stimulus.

11. The system of claim 8 wherein said weighting coefficients are computed such that the weighted responses are maximally discriminating between first and second predetermined times corresponding to two stimuli where a maximum difference in brain activity is expected.

12. The system of claim 11 wherein said brain activity sensors are EEG sensors.

13. The system of claim 11 wherein said brain activity sensors are MEG sensors.

14. The system of claim 8 wherein said linear integration is a summation.