# US 8,542,916 B2 · System and method for analysis of spatio-temporal data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,542,916 B2 - Document ID: US8542916B2 - Status: Granted - Kind: B2 - Application number: 12/500,187 - Filed: 2009-07-09 - Published: 2010-04-22 - Granted: 2013-09-24 - Earliest claimed: 2008-07-09 - Assignees: Florida Atlantic University - Applicants: Emmanuelle Tognoli; J. A. Scott Kelso - Inventors: Emmanuelle Tognoli; J. A. Scott Kelso - Web page: https://patentgrep.com/patent/US8542916B2 ## CPC - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - A61B 5/372: Analysis of electroencephalograms - A61B 5/4094: {Diagnosing or monitoring seizure diseases, e.g. epilepsy} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A system and method of analyzing at least one dataset having temporal and spatial content is provided. A method includes the steps of applying a colorimetric mapping to the dataset based on the spatial content (500), segmenting the dataset (618) into one of a plurality of patterns based on a spatio-temporal analysis of the dataset (604, 606), and analyzing characteristics of each of the plurality of patterns (612-622). ## Claims ### Claim 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. ### Claim 2 (depends on 1) The method of claim 1, wherein said at least one dataset comprises neurophysiologic data. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) 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. ### Claim 5 (depends on 1) 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. ### Claim 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. ### Claim 7 (depends on 6) The system of claim 6, wherein said at least one dataset comprises neurophysiologic data. ### Claim 8 (depends on 6) 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. ### Claim 9 (depends on 6) 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. ### Claim 10 (depends on 6) 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.