Abstract: Biosensing measurements (e.g., heart rate, pupil size, cognitive load, stress level, etc.) are communicated in the context of events that occurred concurrently with the biosensing measurements. The biosensing measurements and the contextual events can be presented in real-time or as historical summaries. Such presentations allow users to easily gain useful insights into which specific events triggered which specific physiological responses in users. Therefore, the present concepts more effectively communicate insights that can be used to change user behavior, modify workflow, design improved products or services, enhance user satisfaction and wellbeing, increase productivity and revenue, and eliminate negative impacts on user's emotions and mental state.

Abstract: The present concepts include a neuroergonomic service that processes multimodal physiological, digital, and/or environmental inputs from a user and predicts cognitive states of the user. Thus, the neuroergonomic service provides personalized feedback to the user about her current mental and physiological wellbeing to enable modulation of mood, stress, attention, and other cognitive measures for improved productivity and satisfaction. The neuroergonomic service utilizes machine learning models that are trained offline using sensor inputs taken from participants in a controlled environment that purposefully induce an array of cognitive states upon the participants.

Abstract: A brain-machine interface system configured to decode neural signals to control a target device includes a sensor to sample the neural signals, and a computer-readable storage medium having software instructions, which, when executed by a processor, cause the processor to transform the neural signals into a common representational space stored in the system, provide the common representational space as a state representation to inform an Actor recurrent neural network policy of the system, generate and evaluate, utilizing a deep recurrent neural network of the system having a generative sequence decoder, predictive sequences of control signals, supply a control signal to the target device to achieve an output of the target device, determine an intrinsic biometric-based reward signal, from the common representational space, based on an expectation of the output of the target device, and supply the intrinsic biometric-based reward signal to a Critic model of the system.

Abstract: A multi-modal bio-sensing apparatus is disclosed including a first sensor module comprising a photoplethysmogram (PPG) sensor configured to produce a first output representative of a blood volume of a human user, wherein the PPG sensor is configured to remove from the first output an error signal due to movement of a user; a second sensor module comprising an electroencephalogram (EEG) sensor configured to produce a third output representative of brain neural activity of the user; a third sensor module comprising an eye-gaze camera configured to capture a gaze direction of one or more eyes of the user; and a wireless communications transceiver coupled to receive sensor data from the first sensor module, the second sensor module, or the third sensor module and configured to wirelessly transmit the received sensor data from the first sensor module, the second sensor module, or the third sensor module out of the multi-modal bio-sensing apparatus.

Abstract: A brain-machine interface system configured to decode neural signals to control a target device includes a sensor to sample the neural signals, and a computer-readable storage medium having software instructions, which, when executed by a processor, cause the processor to transform the neural signals into a common representational space stored in the system, provide the common representational space as a state representation to inform an Actor recurrent neural network policy of the system, generate and evaluate, utilizing a deep recurrent neural network of the system having a generative sequence decoder, predictive sequences of control signals, supply a control signal to the target device to achieve an output of the target device, determine an intrinsic biometric-based reward signal, from the common representational space, based on an expectation of the output of the target device, and supply the intrinsic biometric-based reward signal to a Critic model of the system.