Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Jaehoon Choe

Praveen K Pilly

Matthew E Phillips

Michael D Howard

Steven W Skorheim

Nicholas A Ketz

Heiko Hoffmann

Rajan Bhattacharyya

Michael J Daily

Vincent De Sapio

Matthias Ziegler

Nandan Thor

Dylan T Bergstedt

Yuri Owechko

Kyungnam Kim

Charles E Martin

Kang-Yu Ni

David J Huber

Kenji Yamada

Corey M Thibeault

Iman Mohammadrezazadeh

Tiffany Hwu

Aashish N Patel

Aashish Patel

Michael Howard

A neuro-stimulation system having: a controller; a user interface arranged for, under control of the controller, providing a user with a series of information elements to be learned by the user; a non-invasive brain stimulator arranged for, under the control of the controller, tagging each information element of said series of information elements by stimulating a brain of the user with a different, unique, associated brain stimulus when the user is provided said information element by said user interface; and a non-invasive brain sensor arranged for sending to the controller data indicating a slow-wave sleep period of the user; wherein the controller is further arranged for cueing each information element of said series of information elements by causing the stimulator to stimulate the brain of the user with the brain stimuli associated with said series of information elements during said slow-wave sleep period of the user.

Comprehensive second-language acquisition system leveraging sleep neuromodulation and neuroaugmented executive control

Described is a system for human-machine teaching for vehicle operation. The system determines currently enabled status reporting modes on a vehicle interface of a vehicle. The currently enabled status reporting modes are compared to a set of preferred status reporting modes of previous users. Based on the comparison, a status reporting mode is selected. A current operational status of the vehicle is reported to a current user, via the vehicle interface, using the selected status reporting mode. The system then determines preferred solutions of previous users to address the current operational status of the vehicle. Suggestions to address the current operational status of the vehicle based on the preferred solutions are reported to the user via the vehicle interface. A vehicle action corresponding to a solution selected by the current user is implemented via a vehicle component.

Bidirectional machine teaching interface for human-machine co-pilots

A vehicle and a system and a method of operating the vehicle. The system includes a reasoning engine, an episodic memory, a resolver and a controller. The reasoning engine infers a plurality of possible scenarios based on a current state of an environment of the vehicle. The episodic memory determines a historical likelihood for each of the plurality of possible scenarios. The resolver selects a scenario from the plurality of possible scenarios using the historical likelihoods. The controller operates the vehicle based on the selected scenario.

System for extending functionality of hypotheses generated by symbolic/logic-based reasoning systems

Described is a system for automatic adjustment of neurostimulation. The system controls stimulation of specific neural regions through a neural device positioned on a human subject, while simultaneously performing recordings from the neural device using a targeted arrangement of stimulating electrodes and distinct types of recording electrodes of the neural device. Stimulation of the specific neural regions is adjusted in real-time based on the recordings from the neural device.

Montage design for closed loop sensing and neurostimulation of the dorsal lateral prefrontal cortex and/or motor cortex

Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

System and method for health assessment on smartphones

A closed-loop system for asynchronous brain control of at least one task includes a brain state decoder configured to decode neural signals of a user into control signals for controlling the at least one task, a task interface module configured to transmit the control signals to the at least one task, store status information including a series of messages regarding each of the at least one task, and select one message of the series of messages regarding the at least one task to transmit to the user, and a brain state encoder configured to map the one message received from the task interface module into brain state montages for transmission to the user.

System and method for asynchronous brain control of one or more tasks

A brain-machine interface system configured to decode neural signals to control a target device includes a sensor to sample the neural signals, and a computer-readable storage medium having software instructions, which, when executed by a processor, cause the processor to transform the neural signals into a common representational space stored in the system, provide the common representational space as a state representation to inform an Actor recurrent neural network policy of the system, generate and evaluate, utilizing a deep recurrent neural network of the system having a generative sequence decoder, predictive sequences of control signals, supply a control signal to the target device to achieve an output of the target device, determine an intrinsic biometric-based reward signal, from the common representational space, based on an expectation of the output of the target device, and supply the intrinsic biometric-based reward signal to a Critic model of the system.

System and method for continual decoding of brain states to multi-degree-of-freedom control signals in hands free devices

Described is a system for adaptable neurostimulation intervention. The system monitors a set of neurophysiological signals in real-time and updates a physiological and behavioral model. The set of neurophysiological signals are classified in real-time based on the physiological and behavioral model. A neurostimulation intervention schedule is generated based on the classified set of neurophysiological signals. The system activates electrodes via a neurostimulation intervention system to cause a timed neurostimulation intervention to be administered based on the neurostimulation intervention schedule. The neurostimulation intervention schedule and timed neurostimulation intervention are refined based on new sets of neurophysiological signals.

Method for automated closed-loop neurostimulation for improving sleep quality

Described is a system for transcranial stimulation to improve cognitive function. During operation, the system generates a customized stimulation pattern based on damaged white matter. Further, data is obtained representing natural brain oscillations of a subject. Finally, while the subject is awake, one or more electrodes are activated in phase with the natural brain oscillations and based on the customized stimulation pattern.

Transcranial stimulation system and method to improve cognitive function after traumatic brain injury

The present application generally relates to methods and apparatus for evaluating and assigning a performance metric to a driver response to a driving scenario. More specifically, the application teaches a method and apparatus for breaking a scenario into features, assigning each feature a grade and generating an overall grade in response to a weighted combination of the grades.

Method and apparatus for autonomous system performance and grading

Described is a system for prediction and active compensation of occupant motor response in a vehicle accident. The system uses a spinal reflex model to generate a stimulus based on an accident scenario of an occupant in a vehicle, the stimulus being a set of proprioceptive signals induced by the accident scenario. A neuromuscular model then determines activation and contraction dynamics based on the stimulus. The activation and contraction dynamics represent muscle contraction forces spanning a skeletal system of the occupant. A musculoskeletal model then generates a predicted motor response of the occupant based on the activation and contraction dynamics. The predicted motor response can be used for a variety of purposes, such as initiating active compensation in a vehicle or modifying airline cabin design parameters to decrease the likelihood of injury to the occupant.

System and method for prediction of occupant motor response in accidents

Described is a system for synchronization of neurostimulation interventions. The system continuously monitors incoming neurophysiological signals. Latencies present in the monitoring of the incoming neurophysiological signals are measured. Based on the measured latencies, the timing of targeted neurostimulation interventions is determined, resulting in a neurostimulation intervention protocol. The neurostimulation intervention protocol is adjusted in real time for administration of neurostimulation during temporal regions of interest. The system then triggers administration of the neurostimulation during the temporal regions of interest.

Method for low latency automated closed-loop synchronization of neurostimulation interventions to neurophysiological activity

Described is a system for automatic adjustment of neurostimulation. The system controls stimulation of specific neural regions through a neural device positioned on a human subject, while simultaneously performing recordings from the neural device using a targeted arrangement of stimulating electrodes and distinct types of recording electrodes and sensors of the neural device. Stimulation of the specific neural regions is adjusted in real-time based on the recordings from the neural device.

The present application generally relates to methods and apparatus for evaluating driving performance under a plurality of driving scenarios and conditions. More specifically, the application teaches a method and apparatus for testing a driving scenario repetitively while altering a parametric variation, such as fog level, in order to evaluate driving system performance under changing conditions.

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Agoura Hills, CA, United States of America

Jaehoon Choe