Hrl Laboratories, LLC

Matthias Ziegler

Matthew E Phillips

Rajan Bhattacharyya

Suhas E Chelian

David W Payton

Michael D Howard

Vincent De Sapio

James Benvenuto

Charles E Martin

Jaehoon Choe

Heiko Hoffmann

Praveen K Pilly

David L Allen

Rashmi N Sundareswara

Kevin R Martin

Jeffrey Lawrence Krichmar

Stephanie E Goldfarb

Randall C O'Reilly

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 proactive and reactive cognitive control using a neural module. The system calculates, for each hypothesis of a set of hypotheses, a probability that an event will occur. The neural module comprises a plurality of neurons and includes the PC module, a prefrontal cortex (PFC) module, an anterior cingulate cortex (ACC) module, a locus coeruleus (LC) module, and a basal forebrain (BF) module. The set of hypotheses are related to tasks to be performed by a plurality of groups, each group having a corresponding hypothesis. For each probability, the system calculates a conflict value across all hypotheses with the ACC module, compares each conflict value to a predetermined threshold using the BF and LC modules. A determination is made whether to directly output the calculated probability or perform an additional probability calculation and output an updated probability.

Method and apparatus for proactive and reactive cognitive control using models of the anterior cingulate cortex and the neuromodulatory system

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 online characterization of biomechanical and cognitive factors relevant to physical rehabilitation and training efforts. A biosensing subsystem senses biomechanical states of a user based on the output of sensors and generates a set of biomechanical data. The set of biomechanical data is transmitted in real-time to an analytics subsystem. The set of biomechanical data is analyzed by the analytics subsystem, and control guidance is sent through a real-time control interface to adjust the user's motions. In one aspect control guidance is sent to a robotic exoskeleton worn by the user to adjust the user's motions.

Integrated platform to monitor and analyze individual progress in physical and cognitive tasks

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.

Described is system and method for cognitive recognition. The system receives a multi-dimensional scene array as input data. A foveation module divides the multi-dimensional scene array into a plurality of sub-arrays and outputs contents of a currently selected sub-array. The contents are clustered with a hierarchical clustering module to generate a spatially invariant hierarchical cluster of the contents comprising a plurality of components which are based on a statistical distribution of co-occurrence of features across the currently selected sub-array. Task-relevant components are selectively gated and robustly maintained into a component memory location of a pattern bank with a working memory module with an input gating module. If the task-relevant components activate an abstract category module based on pattern matching, then a category recognition label is generated for the contents of the currently selected sub-array with an executive control module.

Hierarchical clustering method and apparatus for a cognitive recognition system based on a combination of temporal and prefrontal cortex models

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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Oakton, VA, United States of America

Matthias Ziegler