One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

One variation of a method for modeling neurological development includes: aggregating electroencephalography (EEG) data that comprise multiple EEG signals of each user in a set of users, EEG signals of each user recorded on multiple distinct dates, the set of users comprising a plurality of users of various known neurological statuses; identifying a synchronization pattern trend within the EEG data of the set of users; and correlating the synchronization pattern trend with neurological development within the set of users.

Apparatuses and methods for non-invasively detecting and classifying transcranial electrical signals are disclosed herein. In an embodiment, system for detecting and interpreting transcranial electrical signals includes: a headset including a plurality of electrodes arranged for detection of the user's transcranial electrical signals; a display configured to display information to the user while the user wears the headset; and a control unit programmed to: (i) receive data relating to the transcranial electrical signals detected by the electrodes of the headset; (ii) create a data matrix with the received data; (iii) convert the data matrix into one or more user values; (iv) define a user output state based on the one or more user values; and (iv) cause alteration of an aspect of the display based on the user output state.

Apparatuses and methods for non-invasively detecting and classifying transcranial electrical signals are disclosed herein. In an embodiment, system for detecting and interpreting transcranial electrical signals includes: a headset including a plurality of electrodes arranged for detection of the user's transcranial electrical signals; a display configured to display information to the user while the user wears the headset; and a control unit programmed to: (i) receive data relating to the transcranial electrical signals detected by the electrodes of the headset; (ii) create a data matrix with the received data; (iii) convert the data matrix into one or more user values; (iv) define a user output state based on the one or more user values; and (iv) cause alteration of an aspect of the display based on the user output state.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain/cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.

A sensor including electrodes, a control module and a physical layer module. The electrodes are configured to (i) attach to a patient, and (ii) receive a first electromyographic signal from the patient. The control module is connected to the electrodes. The control module is configured to (i) detect the first electromyographic signal, and (ii) generate a first voltage signal. The physical layer module is configured to: receive a payload request from a console interface module or a nerve integrity monitoring device; and based on the payload request, (i) upconvert the first voltage signal to a first radio frequency signal, and (ii) wirelessly transmit the first radio frequency signal from the sensor to the console interface module or the nerve integrity monitoring device.

A sensor including electrodes, a control module and a physical layer module. The electrodes are configured to (i) attach to a patient, and (ii) receive a first electromyographic signal from the patient. The control module is connected to the electrodes. The control module is configured to (i) detect the first electromyographic signal, and (ii) generate a first voltage signal. The physical layer module is configured to: receive a payload request from a console interface module or a nerve integrity monitoring device; and based on the payload request, (i) upconvert the first voltage signal to a first radio frequency signal, and (ii) wirelessly transmit the first radio frequency signal from the sensor to the console interface module or the nerve integrity monitoring device.

Traditional analysis of sleep patterns requires several channel of data. This analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, determining the effect of medication on sleep states and identifying biomarkers, and drug dosages or reactions.

Traditional analysis of sleep patterns requires several channel of data. This analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, determining the effect of medication on sleep states and identifying biomarkers, and drug dosages or reactions.

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.