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Application US20200057498
Published 2020-02-20
Systems And Methods For A Hybrid Brain Interface For Robotic Swarms Using Eeg Signals And An Input Device
A system and a method for hybrid brain computer interface for controlling a robotic swarm are disclosed. The system comprises: a control interface for defining a plurality of electrodes configured for accessing a plurality of brain signals associated with a plurality of channels located over a sensorimotor cortex; a processor, in operative communication with the control interface, configured to: train a machine learning model to associate a brain state with data defining a brain signal, decode a control signal of the plurality of brain signals to generate control data, apply the control data to the machine learning model to identify the brain state, and transmit instructions to a plurality of robotic devices to modify a density of the plurality of robotic devices based on the brain state; and an input device, in operable communication with the processor, for generating input data, the input data utilized by the processor to modify a position of the plurality of robotic devices.
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- 1. A method, comprising:
utilizing a processor in communication with a tangible storage medium storing instructions that are executed by the processor to perform operations comprising:
accessing data associated with a plurality of test Electroencephalographic (EEG) signals, and
training a machine learning model to identify a brain state associated with a predetermined signal of the plurality of test EEG signals using the data;
providing an interface in communication with the processor, the interface configured to access a plurality of control EEG signals; providing an input device in communication with the processor for generating input data associated with a desired position, the desired position associated with a plurality of robotic devices; translating the input data to a first command configured to move the plurality of robotic devices collectively to the desired position; translating a control signal of the plurality of control EEG signals to control data; applying the control data to the machine learning model to identify the brain state; and generating a second command configured to adjust a density associated with the plurality of robotic devices based on the brain state.
- 11. A device, comprising:
a control interface, the control interface defining a plurality of electrodes configured for accessing a plurality of brain signals associated with a plurality of channels located over a sensorimotor cortex; a processor in operative communication with the control interface, the processor configured to:
train a machine learning model to associate a brain state with data defining a brain signal,
decode a control signal of the plurality of brain signals to generate control data,
apply the control data to the machine learning model to identify the brain state, and
transmit instructions to a plurality of robotic devices to modify a density of the plurality of robotic devices based on the brain state; and
an input device in operable communication with the processor for generating input data, the input data utilized by the processor to modify a position of the plurality of robotic devices.
- 16. A device, comprising:
an interface configured for accessing a brain signal; a processor in communication with the interface, the processor configured to decode the brain signal to control data defining a known brain state, the processor further configured to control a first movement associated with a plurality of robotic devices using the control data associated with the known brain state; and an input device in operable communication with the processor for generating input data interpretable by the processor to control a second movement associated with the plurality of robotic devices.
