Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a human-computer interface using high-speed, and efficient tracking of user interactions with a User Interface/User Experience that is strategically presented to the user. Embodiments described herein also relate to the implementation of a hardware agnostic human-computer interface that uses neural, oculomotor, and/or electromyography signals to mediate user manipulation of machines and devices.

Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that integrates real-time eye-movement tracking with brain activity tracking to present and update a user interface that is strategically designed for high speed and accuracy of human-machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface that uses real-time eye tracking and online analysis of neural signals to mediate user manipulation of machines.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a human-computer interface using high-speed, and efficient tracking of user interactions with a User Interface/User Experience that is strategically presented to the user. Embodiments described herein also relate to the implementation of a hardware agnostic human-computer interface that uses neural, oculomotor, and/or electromyography signals to mediate user manipulation of machines and devices.

Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

Abstract: Example methods, apparatus, systems, and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example apparatus includes at least one memory and processor circuitry to execute instructions to identify trough-to-peak instances in first galvanic skin response (GSR) data for a first subject, the first GSR data collected from the first subject during exposure of the first subject to media content during a period of time; assign trough-to-peak scores to corresponding ones of the trough-to-peak instances; associate the respective ones of the trough-to-peak scores with a time corresponding to the trough of each trough-to-peak instances; assign window scores to corresponding ones of windows in the period of time based on the highest trough-to-peak score occurring within the corresponding windows to generate a first GSR intensity profile; and identify an element of the media content for modification based on the first GSR intensity profile.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that integrates real-time eye-movement tracking with brain activity tracking to present and update a user interface that is strategically designed for high speed and accuracy of human-machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface that uses real-time eye tracking and online analysis of neural signals to mediate user manipulation of machines.

Abstract: Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a human-computer interface using high-speed, and efficient tracking of user interactions with a User Interface/User Experience that is strategically presented to the user. Embodiments described herein also relate to the implementation of a hardware agnostic human-computer interface that uses neural, oculomotor, and/or electromyography signals to mediate user manipulation of machines and devices.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that tracks brain activity, with or without additional sensors providing additional sources of information, while presenting and updating a User Interface/User Experience that is strategically designed for high speed and accuracy of human-machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface that uses neural signals to mediate user manipulation of machines and devices.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that integrates real-time eye-movement tracking with brain activity tracking to present and update a user interface that is strategically designed for high speed and accuracy of human-machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface with specific user interface adaptations to enable high-speed, intuitive, and accurate user manipulation of applications and/or machines.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that integrates real-time eye-movement tracking with brain activity tracking to present and update a user interface that is strategically designed for high speed and accuracy of human-machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface that uses real-time eye tracking and online analysis of neural signals to mediate user manipulation of machines.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a human-computer interface using high-speed, and efficient tracking of user interactions with a User Interface/User Experience that is strategically presented to the user. Embodiments described herein also relate to the implementation of a hardware agnostic human-computer interface that uses neural, oculomotor, and/or electromyography signals to mediate user manipulation of machines and devices.

Abstract: Embodiments described herein relate to systems, devices, and methods for use in the implementation of a human-computer interface using high-speed, and efficient tracking of user interactions with a User Interface/User Experience that is strategically presented to the user. Embodiments described herein also relate to the implementation of a hardware agnostic human-computer interface that uses neural, oculomotor, and/or electromyography signals to mediate user manipulation of machines and devices.

Abstract: Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.

Abstract: Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.

Abstract: Example methods, apparatus/systems and articles of manufacture for determining intensity of a biological response to a presentation are disclosed. An example method includes accessing galvanic skin response (GSR) data obtained from a subject while exposed to a presentation. The GSR data includes a plurality of trough-to-peak instances. The example method includes generating a GSR intensity profile by assigning trough-to-peak scores to corresponding ones of the trough-to-peak instances, defining a plurality of time windows, and assigning window scores to corresponding ones of the time windows based on the trough-to-peaks scores of the trough-to-peak instances occurring within the corresponding time windows. The example method also includes determining an effectiveness of the presentation based on the window scores of the GSR intensity profile.