Neuropsychological spatiotemporal pattern recognition

• US 9,730,642 B2
• Filed: 05/05/2015
• Issued: 08/15/2017
• Est. Priority Date: 05/25/2006
• Status: Expired due to Fees

• Current Assignee: ElMindA, Ltd.
• Original Assignee: Elmina Limited
• Inventors: Shahaf, Goded, Geva, Amir B.
• Primary Examiner(s): Pham, Long
• Application Number:
  US14/703,913
• Publication Number:
  US 20150305685A1
• Time in Patent Office: 833 Days
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• 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 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/2323 based on graph theory, e.g. minimum spanning trees [MST] or graph cuts
• CPC Class Codes: G06F 2218/14 by matching peak patterns
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Systems and methods for identifying and analyzing neuropsychological flow patterns, include creating a knowledge base of neuropsychological flow patterns. The knowledge base is formed by obtaining signals from multiple research groups for particular behavioral processes, localizing sources of activity participating in the particular behavioral processes, identifying sets of patterns of brain activity for the behavioral processes and neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups. The neuropsychological analysis includes identifying all possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process and reducing the number of ranked possible pathways based on additional constraints. A system for comparison of obtained signals from an individual to the created knowledge base is provided. These obtained signals are then used to further update the existing knowledge base.

Claims

1. A system for brain activity analysis, the system comprising:

an input module for receiving EEG and/or MEG signals from a subject;

a processor, configured for identifying brain activity patterns in said signals using a counting method or method of calculating statistical significances of pairs, for identifying a plurality of candidate pathways for said brain activity patters, for defining flow patterns among functional brain regions based on candidate pathways, for accessing a flow pattern database having previously determined flow patterns, and for comparing said flow pattern to said previously determined flow patterns of said database; and

an output module for presenting results of said comparison.

2. The system of claim 1, wherein said processor comprises a parser configured for comparing said flow pattern to said previously determined flow patterns of said database.

3. The system of claim 2, wherein said parser is configured to match patterns for a cognitive behavior.

4. The system of claim 2, wherein said parser is configured to match a particular task.

5. The system of claim 1, wherein said output module comprises a workstation including a feedback loop provider configured to receive said comparison and to provide a stimulus to the subject based on said received results.

6. The system of claim 1, wherein said signals comprise EEG signals measured using a plurality of EEG electrodes, and the processor is configured for analyzing waveforms of varying frequencies and amplitudes over time for each EEG electrode.

7. The system of claim 1, wherein said processor is configured for ranking said candidate pathways based on likelihood for a particular behavioral process, prior to said definition of said set of flow patterns, and for reducing a number of candidate pathways based on said ranking.

8. The system of claim 7, wherein said processor is configured for performing an iterative process, and wherein said reducing is based, at least in part, on previously defined flow patterns.

9. The system of claim 7, wherein said reducing is based, at least in part, on at least one temporal constraint.

10. The system of claim 1, wherein said defining said flow patterns comprises using a region matrix.

11. A method of analysis, comprising:

operating an array of EEG and/or MEG electrodes for receiving EEG and/or MEG signals from a subject;

operating a processor for identifying brain activity patterns in said signals using a counting method or method of calculating statistical significances of pairs, for identifying a plurality of candidate pathways for said brain activity patterns, for defining flow patterns among functional brain regions based on candidate pathways, for accessing a flow pattern database having previously determined flow patterns, and for comparing said flow pattern to said previously determined flow patterns of said database;

and

evaluating said subject based on said comparison.

12. The method of claim 11, wherein said at least one set of previously determined flow patterns includes multiple sets of flow patterns, each of said multiple sets corresponding to a different neuropsychological behavior.

13. The method of claim 12, wherein at least one of said multiple sets corresponds to a control group of subjects.

14. The method of claim 11, wherein said array comprises a plurality of EEG electrodes, and the method comprises analyzing waveforms of varying frequencies and amplitudes over time for each EEG electrode.

15. The method of claim 11, further comprising ranking said candidate pathways based on likelihood for a particular behavioral process, prior to said definition of said set of flow patterns, and reducing a number of candidate pathways based on said ranking.

16. The method of claim 15, wherein said obtaining said at least one flow pattern is executed iteratively, and wherein said reducing is based, at least in part, on previously defined flow patterns.

17. The method of claim 15, wherein said reducing is based, at least in part, on at least one temporal constraint.

18. The method of claim 11, wherein said defining said flow patterns comprises using a region matrix.