A large vessel occlusion (LVO) alert generated by a point-of-care diagnostic system is received. The LVO alert is representative of a probability of an LVO event in a patient. A mapping coordinate is received. A database of care centers proximate to the mapping coordinate is accessed. A preferred care center from the database is selected based at least on a treatment capability associated with the preferred care center and a distance of the care centers to the mapping coordinate. An LVO event notification is transmitted to initiate stroke medical care.

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 exemplary magnetic field measurement system includes a wearable sensor unit that includes a magnetometer and a twisted pair cable interface assembly electrically connected to the magnetometer.

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements and body value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.

A processing device receives signals associated with nerve impulses that are transmitted to the visual cortex of a subject in response to one or more visual stimuli provided to at least one eye of the subject. The processing device processes the received signals and generates digital image data from the processed received signals that is representative of the visual perception, by the subject, of the one or more visual stimuli. In certain embodiments, the processing device processes digital image data that is representative of a scene to convert the digital image data to a sequence of nerve impulses, and provides the sequence of nerve impulses to the visual cortex of a subject such that the subject visually perceives the scene.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

A mouth guard senses impact forces and determines if the forces exceed an impact threshold. If so, the mouth guard notifies the user of the risk for injury by haptic feedback, vibratory feedback, and/or audible feedback. The mouth guard system may also remotely communicate the status of risk and the potential injury. The mouth guard uses a local memory device to store impact thresholds based on personal biometric information obtained from the user and compares the sensed forces relative to those threshold values. The mouth guard and its electrical components on the printed circuit board are custom manufactured for the user such that the mouth guard provides a comfortable and reliable fit, while ensuring exceptional performance.

A mouth guard senses impact forces and determines if the forces exceed an impact threshold. If so, the mouth guard notifies the user of the risk for injury by haptic feedback, vibratory feedback, and/or audible feedback. The mouth guard system may also remotely communicate the status of risk and the potential injury. The mouth guard uses a local memory device to store impact thresholds based on personal biometric information obtained from the user and compares the sensed forces relative to those threshold values. The mouth guard and its electrical components on the printed circuit board are custom manufactured for the user such that the mouth guard provides a comfortable and reliable fit, while ensuring exceptional performance.

A clinical response estimate (CRE) biomarker of a patient having an implanted neurostimulation system is monitored. To this end, an input dataset is derived from a subject-patient dataset that includes various different data types and different features of the patient. The data types are based on electrical activity the patient's brain sensed and stored by the implanted neurostimulation system. The input dataset is a subset of the larger subject-patient dataset, and the specific data types and patient features included in that subset are derived based on a plurality of key inputs of the subject-patient dataset. Once the input dataset is derived, it is processed by a clinical response estimator having machine-learned models. First and second machine-learned models of the clinical response estimator are applied to the input dataset to provide model inputs to an ensemble machine-learned model to determine the CRE biomarker.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.