Abstract: In accordance with at least one aspect of this disclosure, an encephalographic electrode assembly can include a wicking element that has a wick body, one or more long legs extending from the wick body and inserted into the one or more hollow contact probes, and one or more short legs, which are shorter than the long legs, extending from the wick body and inserted into the one or more short reservoir sleeves. The short legs are configured to prevent slide-out of the wicking element from the reservoir body.

Abstract: Various methods and systems are provided for cerebral diagnosis. In one example, among others, a method includes obtaining EEG signals from sensors positioned on a subject; conditioning data from the EEG signals to remove artifacts; generating a cerebral network model based at least in part upon the conditioned data; determining network features based upon the cerebral network model; and determining a cerebral condition of the subject based at least in part upon the network features. In another example, a method includes determining a recording condition of a positioned EEG sensor and providing an indication of an unacceptable recording condition of the EEG sensor. In another example, a system includes an EEG recording module to acquire signals; a signal conditioning module to condition signal data; a signal analysis module to determine signal features and cerebral network features; and a condition classification module to determine a cerebral condition of the subject.

Abstract: In accordance with at least one aspect of this disclosure, an encephalographic electrode assembly can include a wicking element that has a wick body, one or more long legs extending from the wick body and inserted into the one or more hollow contact probes, and one or more short legs, which are shorter than the long legs, extending from the wick body and inserted into the one or more short reservoir sleeves. The short legs are configured to prevent slide-out of the wicking element from the reservoir body.

Abstract: Various methods and systems are provided for cerebral diagnosis. In one example, among others, a method includes obtaining EEG signals from sensors positioned on a subject; conditioning data from the EEG signals to remove artifacts; generating a cerebral network model based at least in part upon the conditioned data; determining network features based upon the cerebral network model; and determining a cerebral condition of the subject based at least in part upon the network features. In another example, a method includes determining a recording condition of a positioned EEG sensor and providing an indication of an unacceptable recording condition of the EEG sensor. In another example, a system includes an EEG recording module to acquire signals; a signal conditioning module to condition signal data; a signal analysis module to determine signal features and cerebral network features; and a condition classification module to determine a cerebral condition of the subject.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the chaoticity profiles associated with one or more critical channel groups of a selected predictor, where a predictor represents a given number of critical channel groups “x”, a given number of channels per group “y”, and a total number of channels N. Characterizing the behavior of the system is based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Multiple dynamical profiles are then generated for each spatio-temporal response, where each of the multiple dynamical profiles correspond to a different one of multiple dynamical parameters. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the dynamical profiles associated with a selected one or a selected combination of dynamical parameters. Seizure warnings and/or predictions are provided based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Multiple dynamical profiles are then generated for each spatio-temporal response, where each of the multiple dynamical profiles correspond to a different one of multiple dynamical parameters. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the dynamical profiles associated with a selected one or a selected combination of dynamical parameters. Seizure warnings and/or predictions are provided based on this determination.

Abstract: Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the chaoticity profiles associated with one or more critical channel groups of a selected predictor, where a predictor represents a given number of critical channel groups “x”, a given number of channels per group “y”, and a total number of channels N. Characterizing the behavior of the system is based on this determination.

Abstract: Impending seizure warning (ISW), seizure susceptibility period detection (SSPD), hours or days before an impending seizure, and time to impending seizure prediction (TISP) are provided by measuring each of a number of signals from different locations about a patient's brain, and generating therefrom, a spatio-temporal response based on these signals. Chaoticity profiles are then generated for each spatio-temporal response. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment exists between the chaoticity profiles associated with the responses from a set of critical locations. If so, a seizure warning, an indication of seizure susceptibility and a prediction of a time of occurrence of an impending seizure may be issued.