Home>Methods for Spatiotemporal Analysis of Brain Signals



We frame our studies of complex systems (neural, behavioral, cognitive, social, and their multi-scale arrangements…) within a dynamical framework. Because function emerges from transient interactions between the system’s parts, we are especially interested in description of the system’s coordination dynamics: that is, parts’ transitory influence on each other over the course of their functional choreography. Building up from basic model systems with oscillatory dynamics that exhibit synchronization of their phases, we can theorize, predict, detect and understand the telltale signs of functional interactions in a number of empirical situations.

Methodologically, empirical analysis of coordination dynamics is complicated for systems living in a dimensionality in excess of our representation capabilities. To address this limitation, we have developed visualization techniques that afford 4 or 5 dimensions of spacetime to be grasped perceptually by an observer. This framework avoids the ambiguities of partial spatial or temporal analysis that may split the data in its complementary (temporal or spatial) dimensions at arbitrary and sub-optimal breakpoints. It is already useful in systems governed by state~transition dynamics (that have a coherent spatial and temporal organizational scheme), further, it is essential for the study of spatiotemporally metastable regimes, since the latter have less coincident and more fluid evolvement of their ordering tendencies in space and time.

Another difficulty which is prominent in electrophysiological records of brain activity (EEG, MEG, ECoG, LFP) is the interpretation of measurements from electrical (or magnetic) phenomena. Electric potentials exhibit spatial ubiquity (at the typical rate of our observations, due to near instantaneous spread of its field). When probing the distribution of measurements in a spatial domain, this ubiquity challenges the study of genuine coordination between multiple parts of the systems. We adopt a one-dipolar source-many sensor approach to interpret this spatial distribution meaningfully, and further, we attempt to model multiple sources through an arithmetic of their overlap. within a sparse hypothesis of few simultaneously organized neural ensembles at a time.


Learn more

  • Tognoli, E., Benites, D., Kelso, J.A.S. (in prep). A Blueprint for Brain Coordination Dynamics. [abstract]
  • Tognoli E., Kelso J.A.S. (2009). Brain Coordination Dynamics: True and False Faces of Phase Synchrony and Metastability. Progress in Neurobiology, 87(1): 31-40. [direct link] [abstract] [PDF]
  • Tognoli, E., (2008). EEG coordination dynamics: neuromarkers of social coordination. In Fuchs A, Jirsa VK (eds.) Coordination: Neural, Behavioral and Social Dynamics. Springer. [link] [direct link] [abstract] [PDF]
  • Banerjee, A., Tognoli, E., Assisi, C., Kelso, J.A.S., Jirsa, V.K. (2008). Mode Level Cognitive Subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies. NeuroImage, 15, 663-674. [direct link] [abstract] [PDF]
  • Patent 1. System and method for analysis of spatio-temporal data:
    USPTO 12/500,187 - PCT/US2009/50049 [link] [companion site]


Even more

  • Tognoli, E., (2010). Neural flows in space-time: Traces of the self-organizing brain. Brain Coordination Dynamics 2010, Conference at Sea, Florida & Western Caribbean, March 1st-5th. [link] [direct link]
  • Tognoli, E., Kelso, J.A.S. (2009). A blueprint for the study of abnormal brain coordination dynamics. Dynamical Neuroscience XVII: Dynamical Diseases, Chicago, USA, October 15th-16th. [abstract] [PPT]
  • Tognoli, E., Kelso, J.A.S. (2009). Toward an understanding of 10Hz rhythms in the human waking EEG. Society for Neuroscience Itinerary Planner Program 723.13, Chicago, USA, October 17th-21st. [direct link] [abstract] [PPTX]
  • Tognoli, E. (2009). Reading the mysterious language of the brain: a dynamical challenge for the next decade. Dynamical Systems Seminar, FAU Department of Mathematics, Boca Raton, USA, October 8th. [abstract] [PPTX]
  • Tognoli, E., Kelso, J.A.S. (2008). Recognizing episodes of real synchrony and metastable coordination dynamics in human brain activity. Society for Neuroscience Itinerary Planner Program 814.4, Washington DC, November 15th-19th. [direct link] [abstract] [PPTX]
  • Tognoli E., (2011). EEG source estimation: a discussion on source signals & grey matter constraints. HBBL Meeting, September 29. [PPTX]
  • Tognoli E., Suutari, B., Weisberg, S., Kelso, J.A.S (2011). Dynamics of multiple brain rhythms recruited during individual & social behaviors. HBBL Meeting, October 17. [PPTX]
  • Tognoli, E., (2011). An electrophysiological  view on  self-organization  in brain circuits. February 22. [PPTX]
  • Tognoli E., (2010). Tissue contrast & reconstruction of brain geometries: part I, MRI-based human head models. HBBL meeting, June 17th.[PPTX]
  • Tognoli E., (2009). Anatomy of the head. March 5th. [PPTX]
  • Tognoli, E., (2008). Spectral peaks, spectral floor and EEG temporal scales. HBBL meeting, October 9th. [PPTX]
  • Tognoli E., (2008). From below the surface: Inverse problem, raw EEG and re-reference. HBBL meeting, September 25th. [PPT]
  • Tognoli E., (2008). EEG Coordination Dynamics: brain~mind & brain~body’s functional units. Center for Complex Systems and Brain Sciences, May 20th. [PPT]
  • Tognoli. E., (2007). EEG’s Rosetta stone: Identifying phase-coupling & metastability in the brain. HBBL meeting, June 7th. [PPT]


See also: EEG facility, Center for Complex Systems and Brain Sciences


link to HBBL website link to Center for Complex Systems and Brain Sciences Link to Florida Atlantic University