A transportation system includes an artificial intelligence system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

A method of robotic process automation for achieving an optimized margin of vehicle operational safety includes tracking expert vehicle control human interactions with a vehicle control-facilitating interface, and recording the tracked expert vehicle control human interactions in a robotic process automation system training data structure. The method further includes tracking vehicle operational state information of a vehicle, and recording vehicle operational state information in the robotic process automation system training data structure. The method further includes training, via at least one neural network, the vehicle to operate with an optimized margin of vehicle operational safety in a manner consistent with the expert vehicle control human interactions based on the expert vehicle control human interactions and the vehicle operational state information in the robotic process automation system training data structure, and controlling at least one aspect of the vehicle with the trained artificial intelligence system.

For simplifying the application of electrical stimulation and/or recording of the human brain for therapeutical or diagnostic purposes, an electrode helmet (1) and associated fabrication techniques are provided. The helmet (1) is stable in shape, can be designed to carry a varying number of m electrodes (3) and has a patient-specific geometry that defines the relative position of each electrode with respect to the brain of the patient wearing the helmet (1). This approach improves the accuracy in stimulation and recording as well as the wearing comfort for the patient and allows tailor-made therapy and diagnostic with a component that can be customized at low costs based on a standard design.

For simplifying the application of electrical stimulation and/or recording of the human brain for therapeutical or diagnostic purposes, an electrode helmet (1) and associated fabrication techniques are provided. The helmet (1) is stable in shape, can be designed to carry a varying number of m electrodes (3) and has a patient-specific geometry that defines the relative position of each electrode with respect to the brain of the patient wearing the helmet (1). This approach improves the accuracy in stimulation and recording as well as the wearing comfort for the patient and allows tailor-made therapy and diagnostic with a component that can be customized at low costs based on a standard design.

Devices, systems and methods are disclosed for electrical stimulation of nerves to treat one or more symptoms in a user. The methods comprise transcutaneously transmitting electrical impulses to the nerve according to a treatment paradigm. The treatment paradigm may include generating and transmitting the electrical impulse as a single dose from about 30 seconds to about 5 minutes. The treatment paradigm may comprise a treatment session of 2 to 4 times within an hour time period and/or as a single dose from 2 to 5 times during a day.

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

A system and method is provided for tracking sleep dynamics in a subject. In one aspect, a method includes acquiring physiological data using sensors positioned about the subject, and acquiring behavioral data using input provided by the subject. The method also includes generating a statistical model of wakefulness by combining information obtained from the acquired physiological data and behavior data, and estimating a probability indicative of a degree to which the subject is awake at each point in time during sleep onset using the statistical model. The method further includes generating a report indicative of sleep onset in the subject.

A system and method is provided for tracking sleep dynamics in a subject. In one aspect, a method includes acquiring physiological data using sensors positioned about the subject, and acquiring behavioral data using input provided by the subject. The method also includes generating a statistical model of wakefulness by combining information obtained from the acquired physiological data and behavior data, and estimating a probability indicative of a degree to which the subject is awake at each point in time during sleep onset using the statistical model. The method further includes generating a report indicative of sleep onset in the subject.

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.