System and method for analysis of spatio-temporal data

1. A method of analyzing at least one dataset having temporal and spatial content, the method comprising:

applying a colorimetric mapping to the at least one dataset based on the spatial content to add colorimetric attributes from a colorimetric space to the at least one dataset, wherein the colorimetric mapping uniquely maps spatial coordinates in a physical space comprising the spatial content of the at least one dataset to spatial coordinates in the colorimetric space for selecting the colorimetric attributes;

segmenting the dataset into one of a plurality of patterns based on a spatio-temporal analysis of the dataset; and

analyzing characteristics of each of the plurality of patterns,

wherein the analyzing comprises: registering characteristics of each of the plurality of patterns in the at least one dataset, performing a statistical analysis of said plurality of patterns, classifying the type of dynamics in each of said plurality of patterns based on a taxonomy of patterns, determining a sequential organization of said plurality of patterns, deciphering a mode of coordination dynamics for each of said plurality of patterns, and estimating a source of observed spatio-temporal activity for each of said plurality of patterns based on said deciphering.

2. The method of claim 1 , wherein said at least one dataset comprises neurophysiologic data.

3. The method of claim 1 , wherein said applying further comprises:

selecting the colorimetric space to have a spatial content following the spatial content of the at least one dataset.

4. The method of claim 1 , wherein said segmenting comprises:

localizing phase aggregations in said at least one dataset;

identifying local maxima;

detecting dynamical transitions based on said identified local maxima; and

parsing continuous the at least one dataset into patterns according to the detected dynamical transitions.

5. The method of claim 1 , wherein said deciphering further comprises:

creating time-series to model dynamics of at least one among a single sulcal source, a single gyral source, pairs of uncoupled sources, pairs of coupled sources in-phase, pairs of coupled sources anti-phase, pairs of coupled sources at another phase angle, and pairs of sources with meta-stable dynamics;

applying a forward model to the sequential organization and collecting resulting dynamics at virtual sensors;

identifying critical signatures for each case of virtual neurophysiologic signals in terms of frequency dynamics, relative phase dynamics, and amplitude dynamics;

comparing characteristics of each of the plurality of patterns with said critical signatures; and

inferring an underlying coordination dynamics for each of said plurality of patterns.

6. A system for analyzing spatio-temporal data, the system comprising:

a storage element for receiving at least one dataset having temporal and spatial content;

a processing element configured for:

applying a colorimetric mapping to the at least one dataset based on the spatial content to add colorimetric attributes from a colorimetric space to the at least one dataset, wherein the colorimetric mapping uniquely maps spatial coordinates in a physical space comprising the spatial content of the at least one dataset to spatial coordinates in the colorimetric space for selecting the colorimetric attributes;

segmenting the dataset into one of a plurality of patterns based on a spatio-temporal analysis of the dataset; and

analyzing each of the plurality of patterns based on said selected characteristics

wherein the analyzing comprises: registering characteristics of each of the plurality of patterns in the at least one dataset, performing a statistical analysis of said plurality of patterns, classifying the type of dynamics in each of said plurality of patterns based on a taxonomy of patterns, determining a sequential organization of said plurality of patterns, deciphering a mode of coordination dynamics for each of said plurality of patterns, and estimating a source of observed spatio-temporal activity for each of said plurality of patterns based on said deciphering.

7. The system of claim 6 , wherein said at least one dataset comprises neurophysiologic data.

8. The system of claim 6 , wherein said processing element is further configured during said applying for:

selecting the colorimetric space to have a spatial content following the spatial content of the at least one dataset.

9. The system of claim 6 , wherein processing element is further configured during said segmenting for:

localizing phase aggregations in said at least one dataset;

identifying local maxima;

detecting dynamical transitions based on said identified local maxima; and

parsing continuous the at least one dataset into patterns according to the detected dynamical transitions.

10. The system of claim 6 , wherein said deciphering further comprises:

creating time-series to model dynamics of at least one among a single sulcal source, a single gyral source, pairs of uncoupled sources, pairs of coupled sources in-phase, pairs of coupled sources anti-phase, pairs of coupled sources at another phase angle, and pairs of sources with meta-stable dynamics;

applying a forward model to the sequential organization and collecting resulting dynamics at virtual sensors;

identifying critical signatures for each case of virtual neurophysiologic signals in terms of frequency dynamics, relative phase dynamics, and amplitude dynamics;

comparing characteristics of each of the plurality of patterns with said critical signatures; and

inferring an underlying coordination dynamics for each of said plurality of patterns.