Functional analysis of neurophysiological data
A method for functional analysis of neurophysiological data by decomposing neurophysiological data and EEG signal to form a plurality of signal features. The signal features may then optionally be analyzed to determined one or more patterns.
1. A method of analyzing neurophysiological data, comprising:
using a data processor for:
decomposing the data into a plurality of overlapping sets of waveforms;
identifying data patterns in said waveforms, said data patterns being in at least three-dimensions according to location, latency and frequency of peaks within said waveforms; and
determining brain activity patterns based on said data patterns.
2. The method according to claim 1 , wherein said identifying said data patterns is further according to amplitude to define four-dimensional data patterns.
3. The method according to claim 2 , further comprising clustering said data patterns according to said latency, said frequency and said amplitude.
4. The method according to claim 3 , further comprising combining a collection of said clusters to determine a brain network activity (BNA).
5. The method according to claim 2 , further comprising clustering said data patterns according to said latency, said amplitude and said frequency, to provide a plurality of clusters in at least three-dimensions.
6. The method according to claim 5 , further comprising combining a collection of said clusters to determine a brain network activity (BNA).
7. The method according to claim 1 , further comprising determining a causality relation among data pattern components, and utilizing said relation to determine an activity network among said data patterns.
8. The method according to claim 7 , further comprising determining a brain network activity (BNA) correlation to said activity network.
9. The method according to claim 8 , wherein said determination of said BNA correlation comprises determining synchronization, or lack thereof, between a plurality of areas of the brain.
10. The method according to claim 8 , wherein said identifying said data patterns comprises identifying source localizations for said BNA.
11. The method according to claim 10 , further comprising comparing said data patterns to a previously determined pattern and correcting said source localizations based on said comparison.
12. The method according to claim 1 , further comprising comparing said data patterns to a previously determined pattern.
13. The method according to claim 1 , further comprising searching through a database of previously determined data patterns and selecting from said database a pattern closest to at least one of said identified data patterns.
14. The method according to claim 1 , wherein said neurophysiological data comprise data acquired from multiple subjects for a particular behavioral process, and wherein said brain activity patterns are associated with said behavioral process.
15. The method according to claim 1 , wherein said neurophysiological data comprise data pertaining to a spontaneous brain activity.
16. The method according to claim 1 , wherein said neurophysiological data comprise data acquired before performing a task and data acquired during or after performing said task.
17. The method according to claim 1 , wherein said neurophysiological data comprises EEG signals.
18. The method according to claim 1 , wherein said decomposing the data is effected by a wavelet transform.
19. A method of analyzing neurophysiological data, comprising:
using a data processor for:
decomposing the data into a plurality of waveforms;
identifying in said waveforms data patterns according to location, amplitude, latency and frequency of peaks within said waveforms, to define four-dimensional data patterns;
clustering said data patterns according to said latency, said frequency and said amplitude; and
determining brain activity patterns based on said data patterns.