Example methods, systems, and machine readable media are disclosed herein for determining a subject resonance measurement. An example method includes accessing first neuro-response data obtained from a subject prior to exposure to an advertisement or entertainment and second neuro-response data obtained from the subject after exposure to the advertisement or the entertainment, respectively. The example method includes calculating, using a processor, a first event related potential measurement and a second event related potential measurement based on the first neuro-response data and the second neuro-response data. The example method includes calculating, using the processor, a differential event related potential measurement based on the first event related potential measurement and the second event related potential measurement. In addition, the example method includes determining a subject resonance measurement to the advertisement or the entertainment based on the differential event related potential measurement.

Aspects of embodiments pertain to systems configured to perform neuromonitoring data analysis, by employing the following: receiving patient data comprising data that are descriptive of at least one physical stimulus applied to a mammalian subject for responsively generating at least one signal in a plurality of neural structures of the subject's nervous system; and sensor data descriptive of at least one neurophysiological response signal generated in response the applied physical stimulus. The systems are further configured to determine, based on the received patient data descriptive of the at least one physical stimulus and the generated response signal, at least one characteristic with respect to at least one of the plurality of neural structures of the patient.

The invention provides an advanced, modular, intraoperative neurophysiological monitoring (IONM) system, referred to as a NeuroNet-VII System, which is the first IONM system designed with a USB hub architecture comprising serially-connected functional pods which provides multi-modality simultaneous data acquisition which supports all data types useful in operating rooms, diagnostic laboratories, intensive care units, and epilepsy monitoring units. The unique pod architecture makes the IONM system highly modular compared to current systems which typically place components in a limited number of centralized enclosures. The modular architecture of the invention also provides for real-time collection of data so that information may be communicated with a remotely-located physician; a user needs only to purchase pods that are needed; repair of a single pod may easily be replaced without disabling the entire system; and advances in hardware designs may be implemented for a specific pod without requiring replacement of the entire system.

The invention provides an advanced, modular, intraoperative neurophysiological monitoring (IONM) system, referred to as a NeuroNet-VII System, which is the first IONM system designed with a USB hub architecture comprising serially-connected functional pods which provides multi-modality simultaneous data acquisition which supports all data types useful in operating rooms, diagnostic laboratories, intensive care units, and epilepsy monitoring units. The unique pod architecture makes the IONM system highly modular compared to current systems which typically place components in a limited number of centralized enclosures. The modular architecture of the invention also provides for real-time collection of data so that information may be communicated with a remotely-located physician; a user needs only to purchase pods that are needed; repair of a single pod may easily be replaced without disabling the entire system; and advances in hardware designs may be implemented for a specific pod without requiring replacement of the entire system.

A method of creating a computer implemented classification tool for classification of microelectrode recordings taken during a deep brain stimulation using deep residual neural network with attention comprising steps collecting a data set of recordings taken during a deep brain stimulation; splitting recordings into overlapping time chunks, and converting time chunks into spectrograms; dividing data set into a training set, a validation set, and a test set putting each spectrogram into a deep neural network of ResNet architecture augmented with a self-attention layer added after each of ResNet layers, with a head layer comprising a single 2D convolutional layer followed by batch normalization and ReLU activation function wherein the network is trained to return zero for time chunks taken from recordings made outside of the STN region of a brain and to return one for time chunks taken form recordings made within the STN region of a brain, fine tuning the network with the validation set, cross checking the network with the test set.

A method of creating a computer implemented classification tool for classification of microelectrode recordings taken during a deep brain stimulation using deep residual neural network with attention comprising steps collecting a data set of recordings taken during a deep brain stimulation; splitting recordings into overlapping time chunks, and converting time chunks into spectrograms; dividing data set into a training set, a validation set, and a test set putting each spectrogram into a deep neural network of ResNet architecture augmented with a self-attention layer added after each of ResNet layers, with a head layer comprising a single 2D convolutional layer followed by batch normalization and ReLU activation function wherein the network is trained to return zero for time chunks taken from recordings made outside of the STN region of a brain and to return one for time chunks taken form recordings made within the STN region of a brain, fine tuning the network with the validation set, cross checking the network with the test set.

Systems and Methods for Monitoring Neural Activity A method of monitoring neural activity responsive to a stimulus in a brain or a subject under general anaesthetic, the method comprising: applying the stimulus to one or more of at least one electrode implanted in a target neural structure of the brain; detecting a resonant response from the target neural structure evoked by the stimulus at one or more of the at least one electrode in or near the target neural structure of the brain; and determining one or more waveform characteristics of the detected resonant response.

Systems, devices, and techniques for adjusting electrical stimulation based on a posture state of a patient are described. For example, a system may include sensing circuitry configured to sense an ECAP signal and processing circuitry configured to control delivery of the electrical stimulation to a patient according to a first value of a stimulation parameter and determine a characteristic value of the ECAP signal. The processing circuitry may also be configured to receive, from a sensor, a posture state signal representing a posture state of the patient, determine, based on the posture state signal, a gain value for the stimulation parameter, adjust, based on the characteristic value of the ECAP signal and the gain value, the first value of the stimulation parameter to a second value of the stimulation parameter, and control delivery of the electrical stimulation according to the second value of the stimulation parameter.

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.