On this page we have provided a list (not necessary complete) of our patent claims. Feel free to contact us if you are interested in reviewing, purchasing, or licencing one of these technologies. Some of these patent applications and claims arise from Dr. Zeman’s 2009 dissertation (introduction available) titled, Feasibility of Multi-Component Spatio-Temporal Modeling of Cognitively Generated EEG Data and its Potential Application to Research in Functional Anatomy and Clinical Neuropathology.

We are currently processing claims for 9 separate inventions filed in an international PCT application. These separate inventions are listed below. The body of work from which these claims were derived was filed as part of a provisional US patent on the 16th of April 2010. The PCT application in which the inventions above are described was filed on the 15th of April 2011.

(1) a method or associated apparatus for monitoring functional activity in modular sources within the brain by processing data using a first independent component analysis algorithm to identify a plurality of first components; defining and using a spectral shaping filter that emphasizes frequencies where the first components are more statistically independent than other frequencies to yield spectrally shaped data; processing the spectrally shaped data using a second independent component analysis algorithm to yield second components and determining weights corresponding to the second components.

(2) a method and associated apparatus for obtaining a source value signal corresponding to an area of the brain and measuring a characteristic of the source value signal.

(3) a method and associated apparatus for removing artefacts from an encepholograph by comparing [volume] domain characteristics of processed encephalography data to corresponding thresholds.

(4) an apparatus that has a control unit configured to record encephalography data while generating stimuli displayed to a subject, select one or more portions of the encephalography data where each portion corresponds in time with one of the stimuli and process the portions of the encephalography data.

(5) a method for detecting the effect on brain function of a disease, condition or dysfunction by determining a trajectory of more or more components of encephalography data.

(6) a method for finding source volumes corresponding to encephalography data by using the bias correction of ICA combined with LCMV Beamformer.

(7) a method for validating components of encephalography data as corresponding to specific brain regions based on convergence of the components in iterations of a data mining algorithm.

(8) an artefact cleaning method for encephalography components comprising determining volumes corresponding to the component and assessing whether the components correspond to artefacts based on the corresponding volumes.

(9) a method for real-time processing of encephalography data to yield components corresponding to sources in localized brain regions by applying priori determined weight and sphering matrices to the encephalography data.