Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Matthew E Phillips

Matthias Ziegler

Rajan Bhattacharyya

Vincent De Sapio

Jaehoon Choe

David W Payton

Michael D Howard

Suhas E Chelian

Praveen K Pilly

Michael J Daily

Charles E Martin

Kevin R Martin

James Benvenuto

Nandan Thor

Dylan T Bergstedt

Heiko Hoffmann

David L Allen

Qin Jiang

Kang-Yu Ni

Hankyu Moon

Ryan M Uhlenbrock

Rashmi N Sundareswara

Nicholas A Ketz

Brian L Burns

Michael J O'Brien

Rachel Millin

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 system for mapping user behavior to brain regions of interest. Using a cognitive-behavior model, a behavioral task is selected that is suited for a desired brain state. Using a functional-anatomical model coupled to the cognitive-behavior model, a set of high-definition neurostimulations is selected to be applied to the user during performance of the selected behavioral task. The selected set of high-definition neurostimulations targets specific regions of the user's brain. Changes in the user's brain state are sensed during application of the set of high-definition neurostimulations and performance of the selected behavioral task using at least one brain monitoring technique. The coupled functional-anatomical and cognitive-behavior models are adapted until the desired brain state is reached.

Thinking cap: combining personalized, model-driven, and adaptive high definition trans-cranial stimulation (HD-tCS) with functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) brain state measurement and feedback

Described is a system for automated artifact removal to generate a clean signal. During operation, the system selects initial noise components from a multi-channel, pre-processed signal by performing independent component analysis decomposition on the pre-processed signal to separate and rank the independent components as noise components. A clean signal is then generated through optimized selection of the noise components based on a signal quality index in which the noise components are moved from the original pre-processed signal until a sufficient signal quality is received.

System and method for optimized independent component selection for automated signal artifact removal to generate a clean signal

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 neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

System for mapping extracted Neural activity into Neuroceptual graphs

Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Method and system to predict and interpret conceptual knowledge in the brain

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.

Method and apparatus for scenario generation and parametric sweeps for the development and evaluation of autonomous driving systems

Described is a system for patient-specific rehabilitation that can be performed outside a clinic. The system monitors a patient in real-time to generate a quantitative assessment of a physical state and a motivational state of the patient using sensor data obtained from sensors. Predictions related to the patient are generated utilizing patient-specific biomechanical and neurocognitive models implemented with predictive simulations. Video feed of the patient is registered with sensor data and a set of simulation data. Rehabilitation guidance instructions are conveyed to the patient through dialog-based interactions.

In-home patient-focused rehabilitation system

Described is a system for enhancing memories during sleep. The system detects, via at least one sensor, brain activity of a user as the user learns a new episode pattern or recalls an episode pattern. The episode pattern is temporally compressed. A specific sleep stage is detected in the user. Upon detection of the specific sleep stage, the system automatically recreates the temporally compressed episode pattern by applying neural stimulation to the user to ensure the consolidation of the episode pattern in the user.

Method to enhance specific memories with tCS during slow-wave sleep

Described is a system for system for gait intervention and fall prevention. The system is incorporated into a body suit having a plurality of distributed sensors and a vestibulo-muscular biostim array. The sensors are operable for providing biosensor data to the analytics module, while the vestibulo-muscular biostim array includes a plurality of distributed effectors. The analytics module is connected with the body suit and sensors and is operable for receiving biosensor data and analyzing a particular user's gait and predicting falls. Finally, a closed-loop biostim control module is included for activating the vestibulo-muscular biostim array to compensate for a risk of a predicted fall.

System and method for assistive gait intervention and fall prevention

Described is a system for augmenting team performance via individual neurostimulation. An assessment is generated for each team member of a team while the team member is performing a behavioral task using neuroimaging data. A target brain state is selected in team members for team performance enhancement. The target brain state is associated with specific brain regions, and the system determines a HD-tCS neurostimulation needed to reach the specific brain regions to induce the target brain state in the team members. The determined HD-tCS neurostimulation is applied to the team members while simultaneously sensing, via real-time neuroimaging, neural activity in each team member while the team member performs a behavioral task. Team performance is enhanced by adjusting the HD-tCS neurostimulation of each team member, based on the sensed neural activity, to direct each team member toward the target brain state.

Method for neurostimulation enhanced team performance

Described is a system for rank-ordered and cognitive saliency schema-based object selection. The system receives a set of unnormalized probabilities corresponding to a set of objects competing for attentional selection in a current environment. Each unnormalized probability in the set of unnormalized probabilities is based on a likelihood estimation of encountering the corresponding object in the current environment. The set of objects is ranked based on a set of cognitive saliency values corresponding to the set of objects to generate a rank-ordered list of cognitive saliency values. The rank-ordered list of cognitive saliency values is analyzed to detect a schema of the current environment by which the set of objects is ranked. The schema is learned and stored along with a reward measure of the schema's utility. A maximum saliency object in the set of objects is selected based on the rank-ordered list of cognitive saliency values.

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Calabasas, CA, United States of America

Matthew E Phillips