IMAGING EPILEPSY SOURCES FROM ELECTROPHYSIOLOGICAL MEASUREMENTS

• US 20130096408A1
• Filed: 01/13/2011
• Published: 04/18/2013
• Est. Priority Date: 01/13/2010
• Status: Active Application

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: Regents of The University of Minnesota (University of Minnesota)
• Inventors: He, Bin, Yang, Lin
• Application Number:
  US13/521,990
• Publication Number:
  US 20130096408A1
• Time in Patent Office: Days
• US Class Current: 600/378
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6868 Brain
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

An example includes a method of imaging brain activity. The method includes receiving signals corresponding to neuronal activity of the brain. The signals are based on a plurality of scalp sensors (110). The method also includes decomposing the signals into spatial and temporal independent components (140). In addition, the method includes localizing a plurality of sources corresponding to the independent components. The method includes generating a spatio-temporal representation of neural activity based on the plurality of sources.

Claims

1. A method of imaging brain activity comprising:

receiving signals corresponding to electrical activity of a brain, the signals based on a plurality of scalp sensors;

decomposing the signals into spatial and temporal independent components;

localizing a plurality of sources corresponding to independent components selected based on spatial, temporal or spectral features of interest; and

generating a spatio-temporal representation of electrical activity based on the plurality of sources.

2. The method of claim 1 wherein receiving signals includes at least one of receiving MEG data or receiving EEG data.

3. The method of claim 1 wherein decomposing the signals includes executing an independent component analysis.

4. The method of claim 1 wherein localizing the plurality of sources includes estimating a source distribution using the independent components.

5. The method of claim 1 wherein localizing the plurality of sources includes generating a time-frequency representation of EEG data or generating a time-frequency representation of data corresponding to an independent component.

6. The method of claim 1 wherein generating the spatio-temporal representation includes displaying source distribution within a three dimensional space of the brain.

7. The method of claim 1 further including selecting a surgical intervention site based on the spatio-temporal representation.

8. A system for analyzing electrical activity of an organ, the system comprising:

an input module configured to receive data corresponding to a plurality of signals based on the electrical activity;

a first module configured to decompose the data into independent components;

a second module configured to image a plurality of sources corresponding to the independent components; and

a third module configured to generate a spatio-temporal representation of electrical activity of the organ based on the plurality of sources.

9. The system of claim 8 wherein the input module is configured to couple with a high density array of scalp sensors.

10. The system of claim 9 wherein the scalp sensors include at least one of an EEG sensor or a MEG sensor.

11. The system of claim 8 wherein the input module is configured to couple with at least one intracranial electrode.

12. The system of claim 8 wherein the first module includes a processor configured to implement an independent component analysis algorithm.

13. The system of claim 8 wherein the second module includes a processor configured to estimate a source location corresponding to the independent components.

14. The system of claim 8 wherein the second module includes a processor configured to implement a tomography imaging algorithm.

15. The system of claim 8 wherein the third module is configured to identify a time of onset of seizure based on the spatio-temporal representation.

16. The system of claim 8 further including a display coupled to the third module.