Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

A system for selection and configuration of a robotic process includes a data input module to receive a stream of inputs relating to a user engaged in a task of interest, an input analysis module to analyze the stream of inputs and provide a series of timestamped actions and associated action parameters, and a component selection module to select a component of an AI solution for use in an automated robotic process, based on, at least in part, an action of the series of actions, the associated action parameters, or the components ability to simulate one or more of the actions in the series of actions.

A method for selection and configuration of an automated robotic process includes receiving a temporal biometric measurement of a worker performing a task, receiving a spatial-temporal environmental input provided to the worker, identifying a type of reasoning used when performing the task partially based on the temporal biometric measurement of the worker, selecting a component of an AI solution to replicate the type of reasoning, and configuring the component of the AI solution based on the spatial-temporal environmental input. The temporal biometric measurement includes a set of spatial-temporal imaging data of a brain of the worker and identifying the type of reasoning includes identifying a set of spatial-temporal neocortical activity patterns of the worker, identifying an active area of a neocortex of the worker; and selecting the component of the AI solution partially based on the identified active area of the neocortex.

A method for selecting an AI solution for an automated robotic process including receiving at least one functional media including information indicative of brain activity by a human engaged in a task of interest, analyzing the functional media, identifying an activity level in at least one brain region, identifying a brain region parameter and an activity parameter; identifying an action parameter based in part on the brain region parameter or the activity parameter; and selecting a component of the AI solution in part on the brain region parameter, the activity parameter, or the action parameter.

Brain activity of a user is detected, and a mental state of the user is determined. In one technique, life/work context is automatically presented to the user in a manner that modulates the mental state of the user. In another technique, an entertainment selection is presented to the user, and an entertainment selection list is automatically modified in response to the determined mental state of the user. In still another technique, the mental state that is determined is a negative emotional state, and life/work context is automatically presented to the user in a manner that promotes a cognitive state of the user in response to the determined emotional state of the user. In yet another technique, the wellness of the user is automatically tracked over a time period based on the determined mental state.

Brain activity of a user is detected, and a mental state of the user is determined. In one technique, life/work context is automatically presented to the user in a manner that modulates the mental state of the user. In another technique, an entertainment selection is presented to the user, and an entertainment selection list is automatically modified in response to the determined mental state of the user. In still another technique, the mental state that is determined is a negative emotional state, and life/work context is automatically presented to the user in a manner that promotes a cognitive state of the user in response to the determined emotional state of the user. In yet another technique, the wellness of the user is automatically tracked over a time period based on the determined mental state.

A system includes a controllable device configured to provide a premises related service in an area. The system includes a neural device to be positioned with respect to a part of a body of a user and circuitry to process nerve signals detected in real-time. The system also includes a processor in communication with the circuitry, a memory and instructions stored in the memory for execution by the processor. Data stored in the memory associates each of some number of predetermined sets of nerve signals with a control instruction. Execution of the instructions configures the processor to use the stored data to analyze the real-time detected nerve signals to determine when real-time detected nerve signals correspond to one of the predetermined sets of nerve signals and generate a control data signal based on the associated control instruction to cause a controller to control an operation of the controllable device.

Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

Brain activity of a user is detected, and a mental state of the user is determined. In one technique, life/work context is automatically presented to the user in a manner that modulates the mental state of the user. In another technique, an entertainment selection is presented to the user, and an entertainment selection list is automatically modified in response to the determined mental state of the user. In still another technique, the mental state that is determined is a negative emotional state, and life/work context is automatically presented to the user in a manner that promotes a cognitive state of the user in response to the determined emotional state of the user. In yet another technique, the wellness of the user is automatically tracked over a time period based on the determined mental state.

A method includes receiving, by a mobile computing device from an electroencephalogram (EEG) monitoring headset, an incoming wireless communication signal including an EEG data stream. The method may further include processing, by an application running on the mobile computing device, the received EEG data stream to determine at least one actionable command for at least one peripheral device. The method may also include transmitting, by the mobile computing device to the at least one peripheral device, at least one outgoing wireless communication signal including the at least one determined actionable command.