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: 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: According to various embodiments, an apparatus includes an adhesive layer disposed on a first side of a porous layer. A plurality of bound wire mesh layers are attached to a second side of the porous layer. One or more pores of the porous layer correspond to one or more air gaps between the bound wire mesh layers and the porous layer. The apparatus further includes a characteristic of a presence of an extent of surface tension at (or throughout) the bound wire mesh layers. Each of the one or more air gaps further corresponds to one or more opening portions of a wire strand layer(s) in the bound wire strand layers. Other embodiments are directed to a method to configure the apparatus.

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: 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 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.