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 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.

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.

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.

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.

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.

Systems and methods are described herein which may be implemented using computer programs comprising instructions that replicate the neural synchronization algorithm of the human brain. These implementations result in reduction, optimization, security and acceleration of data records/frames and processing in a computer system or network. An embodiment of the invention comprises motion decimation, motions reactor, motion replicator and motion aggregator modules for replicating higher intelligence functions, and a management module for configuring resources and monitoring system operation. Systems and methods as described herein may operate on wired or wireless computer networks. Data are translated from original format into thalamic motion and further encoded with motion signal protocol, then reproduced and aggregated using thalamic motion for integration with higher forms of intelligence. By duplicating the human brain's neural synchronization process, overall process and communication efficiency may be dramatically increased.

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.