An apparatus includes data acquisition circuitry and a processor. The data acquisition circuitry is configured to acquire multiple signals using multiple respective electrodes of an array of electrodes coupled to one of an organ of a patient and tissue or a cell culture. The processor is configured to hold a definition of a mixed-norm that is defined as a function of relative positions of the electrodes in the array, and jointly compress the multiple signals in a compressed-sensing (CS) process that minimizes the mixed-norm.

A modular, multi-modal energy therapy system for electrical and electromagnetic stimulation includes signal generating, conditioning, and control electronics, stimulation monitoring electronics, signal conduits, and wearable energy emitter modules configured for coupling energy emitters to surfaces of the human ear for transcutaneous energy delivery to nerves in the auricular nerve field. Electrical emitter modules configured with electrodes deliver electrical stimulation; electromagnetic emitter modules configured with light emitting diodes deliver electromagnetic stimulation. A computer controls signal generating electronics and provides internet connectivity with a remote server. Application software includes stimulation programming and parameter selection, and databases containing user data, records of stimulation sessions, user responses to symptom assessment instruments, and biofeedback sensor input enable local and remote monitoring of a user's health status by therapists.

Methods and systems for conditioning a signal indicative of electrosurgical unit activity are described. A hardware circuit acquires AC current from an electrosurgical unit on patient isolated circuitry and conditions the signal in either of two alternate processing methods. The processed signal is routed as input to an analog to digital converter circuit. A method for determining saturation on referential inputs and recovering inputs to an unsaturated state is also described.

A physiological sensor history backfill system and method including a method of sensor history backfill for a local base device operable to wirelessly communicate with a physiological sensor connected to a patient, the method including: obtaining physiological readings for the patient at a predetermined interval; storing the physiological readings at the physiological sensor as sensor physiological readings; storing the physiological readings at the local base device as historic physiological readings; obtaining a current physiological reading for the patient; transmitting the current physiological reading to the local base device in a current reading message; detecting a record gap in the historic physiological readings between the current physiological reading and the historic physiological readings; and filling the record gap in the historic physiological readings with sensor physiological readings from the physiological sensor when the current reading message does not include the sensor physiological readings to fill the record gap.

Computer-implemented methods and automated systems for real-time detection of spreading depolarizations in a brain injured patient, based an algorithm of (a) providing a reference data base of spreading depolarization waveform templates generated from EEG recordings of confirmed spreading depolarizations (SD) in a reference brain-injured patient cohort; (b) recording an EEG of the brain injured patient to generate recorded EEG waveforms; (c) detecting a slow potential change present in a recorded EEG waveform by applying a power spectral density estimate to the recorded waveform; (d) comparing a detected SPC to a reference database of SD waveform template to identify a candidate SD; and (e) rejecting a candidate SD as a false positive based on overall signal power and amplitude analysis and identifying a non-rejected candidate SD as a detected SD.

According to some aspects a system is provided comprising a low-field magnetic resonance (MR) device, at least one electrophysiological device, and at least one controller configured to operate the low-field MR device to obtain MR data and to operate the at least one electrophysiological device to obtain electrophysiological data.

A method for titrating a stimulation parameter for one or more electrode contacts in a system for stimulating a hypoglossal nerve of a patient includes activating one of the one or more electrode contacts to stimulate the hypoglossal nerve of the patient, obtaining a first and/or second physiological measurement from the patient, comparing the first and/or second physiological measurement to a first and/or second predetermined target value, adjusting a stimulation parameter for the one of the one or more electrode contacts if the first and/or second physiological measurement differs from the first and/or second predetermined target value.

An apparatus and method for removing high-frequency radio frequency interference are disclosed. The apparatus includes a filtering unit for filtering an original electroencephalogram signal to obtain an intermediate electroencephalogram signal; a high-frequency signal extraction unit for filtering the intermediate electroencephalogram signal to extract a high-frequency signal from the intermediate electroencephalogram signal; a marking unit for comparing the amplitude of the high-frequency signal with a comparison threshold value, and performing classification marking on the intermediate electroencephalogram signal according to a comparison result; a collection unit for sampling the intermediate electroencephalogram signal that has been performed with the classification marking to obtain a desired electroencephalogram signal and inputting the desired electroencephalogram signal into a processor; and the processor for setting the comparison threshold value and controlling the collection unit to perform sampling.

Described herein is a system and method for controlling a computing system by an AI network based upon an electroencephalograph (EEG) signal from a user. The user's EEG signals are first detected as the user operates an existing controller, during which time the associated artificial intelligence (AI) system learns by correlating the EEG signals with the commands received from the controller. Once the AI system determines that there is sufficient correlation to predict the user's actions, it can take control of the computing system and initiate commands based upon the user's EEG signal in place of the user's actions with the controller. At this point, weights in the AI network may be locked so that further commands from the controller, or the lack thereof, do not reduce correlation with the EEG signals. In some embodiments, the AI network may initiate commands faster than the user would be able to do.

An interactive chair with an optional headset comprising integrated electrocardiogram and electroencephalography sensors and pulsed electromagnetic field generators along with other input and outputs necessary for a user's experience to be manipulated by a computer from the user's present state, ab initio, or from the user's present state in conjunction with other media; thereby allowing a user of the chair to integrate a wide variety of physical and electromagnetic biofeedback experiences into their daily lives. When used in conjunction with media, the chair allows the user to experience media in a manner that is not predetermined by the media alone, but also by the user's current physical experience; thus, adding additional variable dimensions to the experience of the media.