A system and method for brainwave entrainment using virtual objects and gamification, in which brainwave entrainment is applied using some combination of gaming elements, brainwave is enhanced by virtue of the user’s active participation, and long-term use is encouraged by virtue of the entertaining nature of the gamification. Depending on configuration, the system and method may comprise a display comprising virtual objects, a light-producing device (other than the display), an audio-producing device such as speakers or headphones, a haptic feedback device such as a vibratory motor, a means for monitoring the user’s attention, and a software application which applies brainwave entrainment using some combination of the display, the light-producing device, the audio-producing device, and the haptic feedback device.

A system and method for brainwave entrainment using virtual objects and gamification, in which brainwave entrainment is applied using some combination of gaming elements, brainwave is enhanced by virtue of the user’s active participation, and long-term use is encouraged by virtue of the entertaining nature of the gamification. Depending on configuration, the system and method may comprise a display comprising virtual objects, a light-producing device (other than the display), an audio-producing device such as speakers or headphones, a haptic feedback device such as a vibratory motor, a means for monitoring the user’s attention, and a software application which applies brainwave entrainment using some combination of the display, the light-producing device, the audio-producing device, and the haptic feedback device.

A cognitive load estimation system. The system includes an-in ear device (IED) configured to be placed within an ear canal of a user. The IED includes a first set of functional near-infrared spectroscopy (fNIRS) optodes that are configured to capture first fNIRS signal data representing hemodynamic changes in a brain of the user. The system further includes at least one electroencephalography (EEG) electrode configured to capture electrical signals corresponding to brain activity of the user. The system further includes a controller configured to filter the first fNIRS signal data based in part on the electrical signals to generate filtered fNIRS signal data. The controller is further configured to estimate a cognitive load of the user based on the filtered fNIRS signal data.

A cognitive load estimation system. The system includes an-in ear device (IED) configured to be placed within an ear canal of a user. The IED includes a first set of functional near-infrared spectroscopy (fNIRS) optodes that are configured to capture first fNIRS signal data representing hemodynamic changes in a brain of the user. The system further includes at least one electroencephalography (EEG) electrode configured to capture electrical signals corresponding to brain activity of the user. The system further includes a controller configured to filter the first fNIRS signal data based in part on the electrical signals to generate filtered fNIRS signal data. The controller is further configured to estimate a cognitive load of the user based on the filtered fNIRS signal data.

A system for transportation includes a vehicle, a rider occupying the vehicle, and a hybrid neural network. The hybrid neural network includes a first neural network to process a sensor input corresponding to the rider to determine an emotional state of the rider, and a second neural network to optimize at least one operating parameter of the vehicle to improve the emotional state of the rider.

A system for transportation includes a vehicle occupied by a rider, an electronic commerce system of the vehicle, and an artificial intelligence system for processing data from an interaction of the rider with the electronic commerce system to classify a rider state and optimizing at least one operating parameter of the vehicle to improve the rider state.

A motorcycle helmet includes a data processor configured to facilitate communication between a rider wearing the helmet and a motorcycle, the motorcycle and the helmet communicating location and orientation of the motorcycle. An augmented reality system with a display is disposed to facilitate presenting an augmentation of content in an environment of a rider wearing the helmet, the augmentation responsive to a registration of the communicated location and orientation of the motorcycle At least one parameter of the augmentation is determined by machine learning on at least one input relating to at least one of the rider and the motorcycle.

A system for transportation includes a vehicle interface for gathering hormonal state data of a rider in the vehicle. The system further includes an artificial intelligence-based circuit that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes. The set of outcomes includes at least one outcome that promotes rider safety. The inducing variation includes control of timing and extent of the variation.

A system for transportation includes a vehicle having at least one rider located in the vehicle and a data processing system for taking data from a plurality of social data sources. A hybrid neural network is connected to the data processing system. The system for transportation is to optimize satisfaction of the at least one rider based on processing the data from the plurality of social data sources with the hybrid neural network.

A transportation system includes an artificial intelligence (AI) 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 AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.