Abstract: A brain-computer interface system includes a video processor for producing a display signal, a temporal controller for producing a plurality of repetitive visual stimulus (RVS) signals with different respective temporal aspects, a display device that receives the display signal and displays a corresponding image on a plurality of different display regions and receives the RVS signals and displays corresponding RVS in respective ones of the display regions, an electroencephalographic (EEG) sensor for sensing a visually-evoked cortical potential (VECP) signal in a user with eyes fixated on a viewed one of the display regions, and a VECP processor for processing the VECP signal to identify the respective temporal aspect of the respective RVS of the viewed display region to estimate the eye fixation location. The RVS are generated independently of the display update/refresh rate and at sufficiently high frequencies to avoid flicker perceptible to the user.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.

Abstract: A dynamic function allocation (DFA) framework balances the workload or achieves other mitigating optimizations for a human operator of a vehicle by dynamically distributing operational functional tasks between the operator and the vehicle's or robot's automation in real-time. DFA operations include those for aviation, navigation, and communication, or to meet other operational needs. The DFA framework provides an intuitive command/response interface to vehicle (e.g., aircraft), vehicle simulator, or robotic operations by implementing a Dynamic Function Allocation Control Collaboration Protocol (DFACCto). DFACCto simulates or implements autonomous control of robot's or vehicle's functional tasks and reallocates some or all tasks between a human pilot and an autonomous system when such reallocation is determined to be preferred, and implements the reallocation.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.

Abstract: Aspects of the present disclosure are directed to devices, systems, and methods for optimized integration of a human operator with a machine for safe and efficient operation. Accordingly, aspects of the present disclosure are directed to systems, methods, and devices which evaluate and determine a cognitive state of an operator, and allocate tasks to either the machine and/or operator based on the cognitive state of the operator, among other factors.