Abstract: Methods and apparatus for identifying and using at least one nonlinear feature determined from multiscale electroencephalography (EEG) data to evaluate an epileptogenicity level of a patient is described. A multiscale algorithm is applied to EEG data recorded from the patient to produce scaled EEG data. At least one nonlinear feature value for the received EEG data and/or the scaled EEG data is determined and the at least one nonlinear feature value is used, at least in part, to evaluate the epileptogenicity level of the patient.

Abstract: Methods and apparatus for identifying and using at least one nonlinear feature determined from multiscale electroencephalography (EEG) data to evaluate an epileptogenicity level of a patient is described. A multiscale algorithm is applied to EEG data recorded from the patient to produce scaled EEG data. At least one nonlinear feature value for the received EEG data and/or the scaled EEG data is determined and the at least one nonlinear feature value is used, at least in part, to evaluate the epileptogenicity level of the patient.

Abstract: The nonlinear complexity of EEG signals is believed to reflect the scale-free architecture of the neural networks in the brain. Analysis of the complexity and synchronization of EEG signals as described herein provides a quantitative measure for routine monitoring of functional brain development in infants and young children and provide a useful biomarker for detecting functional abnormalities in the brain before the cognitive, behavioral or social manifestations of these brain developments can be observed and measured by standard tests. One or more machine learning algorithms are used to discover relevant patterns in the complexity and synchronization values determined from the EEG data to facilitate risk assessment and/or diagnosis of developmental disorders in infants and young children by predicting cognitive, behavioral and social outcomes of the measured functional brain activity patterns.

Abstract: A method for estimating a physical property of a porous material from a sample of the material includes making a three dimensional tomographic image of the sample of the material, segmenting the image into pixels each representing pore space or rock grain, and estimating at least one physical property from the segmented image.

Abstract: A method for estimating a physical property of a porous material from a sample of the material includes making a three dimensional tomographic image of the sample of the material, segmenting the image into pixels each representing pore space or rock grain, and estimating at least one physical property from the segmented image.