Optios, Inc.

Neuromatters, LLC

Paul DeGuzman

Paul Sajda

Jacek Dmochowski

Steven Lamont Miller

Suhas E Chelian

Amy Kruse

Thomas F Nugent, Iii

Will McBurnett

Lucas Cristobal Parra

Daniel Rosenthal

David Bach

Charles Phillip Cloud

Sam DeWitt

Ben Londergan

Pawel Gucik

A method includes generating a trading performance model for a trading activity involving a set of decisions by a set of expert traders. The trading performance model includes a set of input data sets, a set of data processing workflows operating on the input data sets, and a set of trading decision outputs resulting from interaction of the expert traders with a user interface representing the trading performance model. The method includes generating a brain state model representing a sequential set of brain states of the set of expert traders that characterize brain states measured during the interactions of the expert traders with the user interface representing the trading performance model, assessing the quality of the trading decisions, determining a preferred pattern of trader brain state sequences, and modifying a subsequent trading activity.

Optimization of trading performance using both brain state models and operational performance models

To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data—for example, clusters of correlations of decomposed data envelopes—grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.

Identifying and strengthening physiological/neurophysiological states predictive of superior performance

To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data&#x2014;for example, clusters of correlations of decomposed data envelopes&#x2014;grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.

Methods are disclosed for determining an efficacy of a stimulus based on one or more measurable physiological responses to one or more stimuli including one or more stimulus features. Data is acquired on physiological responses of a group of one or more subjects to presentation of one or more stimuli including one or more stimulus features. The data on the one or more physiological responses of the one or more subjects is correlated with the presentation of the one or more stimulus features included in the one or more stimuli. The correlated data on the one or more physiological responses are associated with a separately-determined efficacy of the one or more stimuli to form a stimulus efficacy model. From this information, a projected efficacy of a stimulus is determinable by comparing one or more subsequently-measured physiological responses to the stimulus with the stimulus efficacy model.

Method for predicting efficacy of a stimulus by measuring physiological response to stimuli

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Valley Cottage, NY, United States of America

Paul DeGuzman