BMI controlled robots with semi-autonomous capabilities

The primary objective of this project is to develop an EEG-based brain-machine interface (BMI) for controlling an adaptive mobile agent. Specifically, the agent is an iRobot Create enhanced with a rotatable camera and robotic arm. Using EEG signals, subjects will be tasked with navigating the robot to a desired location in a room, orienting the camera to fixate upon a target object, and picking up the attended object with the robotic arm. This complex task will be broken into two major components: 1) EEG-based robotic navigation / object selection and 2) biologically inspired, autonomous and goal-directed robotic movement and arm control.  To accomplish this task we employ a two-way co-adaptation paradigm where both the subject and robot adapt to each other. The subject learns to use EEG signals to improve control by practicing movements; this type of learning has proven crucial to BMI performance in other domains. A computer onboard the robot will use adaptive algorithms to continually improve its ability to identify the key EEG signal components signaling the subject’s intent.

This collaborative effort combines research in the Neural Prosthesis Lab, for decoding 2D movements via EEG of motor imagery, and the Neuromorphics Lab for developing sensorimotor communication software for the iRobot Create mobile robotics platform. This software  allows for complex signal processing and model execution to occur separate from the physical robot and enables relatively easy integration of EEG decoding software with the neural models underlying the robot’s behavior.

The project has significant potential for clinical applications. Patients suffering from severe motor impairment could regain some agency through control of these mobile devices, increasing their quality of life. Novel commercial applications for healthy subjects are also possible.

Main personnel Byron Galbraith
Collaborators Max Versace, Jon Brumberg, Frank Guenther CELEST: A brain-machine interface for assistive robotic control