Abstract: The present subject matter relates to techniques for hierarchical deep transcoding. The disclosed system can include a processor that can be configured to receive a functional magnetic resonance imaging (fMRI) data and/or an extracranial electroencephalogram (EEG) data and reconstruct a latent source space from the fMRI data and/or the EEG data by decoding the EEG data and/or the fMRI data to a latent source space. The fMRI data and the EEG data can be simultaneously acquired.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates to systems and methods for providing a hybrid brain-computer-interface (hBCI) that can detect an individual's reinforcement signals (e.g., level of interest, arousal, emotional reactivity, cognitive fatigue, cognitive state, or the like) in and/or response to objects, events, and/or actions in an environment by generating reinforcement signals for improving an AI agent controlling the environment, such as an autonomous vehicle. Although the disclosed subject matter is discussed within the context of an autonomous vehicle virtual reality game in the exemplary embodiments of the present disclosure, the disclosed system can be applicable to any other environment in which the human user's sensory input is to be used to influence actions within the environment. Furthermore, the systems and methods disclosed can use neural, physiological, or behavioral signatures to inform deep reinforcement learning based AI systems to enhance user comfort and trust in automation.

Abstract: The present subject matter relates to techniques for systems and methods for determining alpha phase in brain of subjects undergoing depressive disorder. The disclosed system for a closed-loop operation in simultaneous functional magnetic resonance imaging (fMRI)-electroencephalogram (EEG)-transcranial magnetic stimulation (TMS), can include a processor that be configured to receive and process a functional magnetic resonance imaging (fMRI) data and/or an extracranial electroencephalogram (EEG) data and/or transcranial magnetic stimulation (TMS) pulse simultaneously.

Abstract: 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.

Abstract: The present subject matter relates to techniques for hierarchical deep transcoding. The disclosed system can include a processor that can be configured to receive a functional magnetic resonance imaging (fMRI) data and/or an extracranial electroencephalogram (EEG) data and reconstruct a latent source space from the fMRI data and/or the EEG data by decoding the EEG data and/or the fMRI data to a latent source space. The fMRI data and the EEG data can be simultaneously acquired.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems for evaluating a subject's response to a task related to a stimulus include a brain activity sensor, such as an EEG sensor, for measuring neural data generated by the subject in response to the visual stimulus. One or more neural discriminators can be calculated based on the neural data. In order to generate one or more neural discriminators, two or more task conditions can be selected for discrimination. The subject's performance can be evaluated based on the one or more neural discriminators. Feedback can be provided to the subject to assist in achieving better performance.

Abstract: The present disclosure relates to systems and methods for providing a hybrid brain-computer-interface (hBCI) that can detect an individual's reinforcement signals (e.g., level of interest, arousal, emotional reactivity, cognitive fatigue, cognitive state, or the like) in and/or response to objects, events, and/or actions in an environment by generating reinforcement signals for improving an AI agent controlling the environment, such as an autonomous vehicle. Although the disclosed subject matter is discussed within the context of an autonomous vehicle virtual reality game in the exemplary embodiments of the present disclosure, the disclosed system can be applicable to any other environment in which the human user's sensory input is to be used to influence actions within the environment. Furthermore, the systems and methods disclosed can use neural, physiological, or behavioral signatures to inform deep reinforcement learning based AI systems to enhance user comfort and trust in automation.

Abstract: Human visual perception is able to recognize a wide range of targets but has limited throughput. Machine vision can process images at a high speed but suffers from inadequate recognition accuracy of general target classes. Systems and methods are provided that combine the strengths of both systems and improve upon existing multimedia processing systems and methods to provide enhanced multimedia labeling, categorization, searching, and navigation.

Abstract: Methods and systems for evaluating a subject's response to a task related to a stimulus include a brain activity sensor, such as an EEG sensor, for measuring neural data generated by the subject in response to the visual stimulus. One or more neural discriminators can be calculated based on the neural data. In order to generate one or more neural discriminators, two or more task conditions can be selected for discrimination. The subject's performance can be evaluated based on the one or more neural discriminators. Feedback can be provided to the subject to assist in achieving better performance.

Abstract: Methods and systems for evaluating a subject's response to a task related to a stimulus include a brain activity sensor, such as an EEG sensor, for measuring neural data generated by the subject in response to the visual stimulus. One or more neural discriminators can be calculated based on the neural data. In order to generate one or more neural discriminators, two or more task conditions can be selected for discrimination. The subject's performance can be evaluated based on the one or more neural discriminators. Feedback can be provided to the subject to assist in achieving better performance.

Abstract: An EEG cap (8) having 64 or 128 electrodes (10) is placed on the head of the subject (11) who is viewing CRT monitor (14). The signals on each channel are amplified by amplifier (17) and sent to an analog-to-digital converter (20). PC (23) captures and records the amplified signals and the signals are processed by signal processing PC (26) performing linear signal processing. The resulting signal is sent back to a feedback/display PC (29) having monitor (14). An EEG cap (8) having 64 or 128 electrodes (10) is placed on the head of the subject (11) who is viewing CRT monitor (14). The signals on each channel are amplified by amplifier (17) and sent to an analog-to-digital converter (20). PC (23) captures and records the amplified signals and the signals are processed by signal processing PC (26) performing linear signal processing. The resulting signal is sent back to a feedback/display PC (29) having monitor (14).

Abstract: An EEG cap (8) having 64 or 128 electrodes (10) is placed on the head of the subject (11) who is viewing CRT monitor (14). The signals on each channel are amplified by amplifier (17) and sent to an analog-to-digital converter (20). PC (23) captures and records the amplified signals and the signals are processed by signal processing PC (26) performing linear signal processing. The resulting signal is sent back to a feedback/display PC (29) having monitor (14).

Abstract: Human visual perception is able to recognize a wide range of targets but has limited throughput. Machine vision can process images at a high speed but suffers from inadequate recognition accuracy of general target classes. Systems and methods are provided that combine the strengths of both systems and improve upon existing multimedia processing systems and methods to provide enhanced multimedia labeling, categorization, searching, and navigation.

Abstract: Human visual perception is able to recognize a wide range of targets but has limited throughput. Machine vision can process images at a high speed but suffers from inadequate recognition accuracy of general target classes. Systems and methods are provided that combine the strengths of both systems and improve upon existing multimedia processing systems and methods to provide enhanced multimedia labeling, categorization, searching, and navigation.