Abstract: Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and/or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and/or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

Abstract: A method for non-invasively resolving electrophysiological activity in sub-cortical structures located deep in the brain by comparing amplitude-insensitive M/EEG field patterns arising from activity in subcortical and cortical sources under physiologically relevant sparse constraints is disclosed. The method includes a sparse inverse solution for M/EEG subcortical source modeling. Specifically, the method employs a subspace-pursuit algorithm rooted in compressive sampling theory, performs a hierarchical search for sparse subcortical and cortical sources underlying the measurement, and estimates millisecond-scale currents in these sources to explain the data. The method can be used to recover thalamic and brainstem contributions to non-invasive M/EEG data, and to enable non-invasive study of fast timescale dynamical and network phenomena involving widespread regions across the human brain.

Abstract: A method for non-invasively resolving electrophysiological activity in sub-cortical structures located deep in the brain by comparing amplitude-insensitive M/EEG field patterns arising from activity in subcortical and cortical sources under physiologically relevant sparse constraints is disclosed. The method includes a sparse inverse solution for M/EEG subcortical source modeling. Specifically, the method employs a subspace-pursuit algorithm rooted in compressive sampling theory, performs a hierarchical search for sparse subcortical and cortical sources underlying the measurement, and estimates millisecond-scale currents in these sources to explain the data. The method can be used to recover thalamic and brainstem contributions to non-invasive M/EEG data, and to enable non-invasive study of fast timescale dynamical and network phenomena involving widespread regions across the human brain.