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Relevant Projects

Photo of Miriam Zacksenhouse
Associate Professor
Non-invasive Brain-Computer Interfaces

Non-invasive brain computer interfaces (BCIs) provide direct communication link from the brain to external devices. We develop non-invasive BCIs that are based on interpreting EEG measurements to identify user’s desired selection, action or movement. We focus on developing self- correction capabilities, based on error-related potentials (ErrPs), which are evoked in the brain when errors are detected.  We investigate ErrPs, develop classifiers for detecting them and methods to integrate them to improve BCIs. This project is funded by Dr. Maria Ascoli Rossi Research Grant.

Invasive Brain-Machine Interfaces

Invasive Brain-Machine Interfaces (BMIs) provide direct communication link from the brain to external devices. Invasive BMIs are based on interpreting neural activity recorded with invasive electrodes, identifying desired movements and controlling external devices accordingly. We develop algorithms to identify error-related processing in the neural activity and to correct the BMIs accordingly. This project is performed in collaboration with Chestek’s Lab at the University of Michigan and funded by Betty and Dan Kahn Foundation.

Reinforcement learning of assembly policies

Our research focuses on developing control policies that are based on admittance control to facilitate learning and sim2real. This is part of a Large project on Assembly by Robotic Technology (ART) funded by the Israel Innovation Authority. We developed a Residual Admittance Policy (RAP) that generalizes well over space, size and shape, and facilitates quick transfer learning. Most impressively, we demonstrate that the policy learned in simulations is highly successful in controlling an industrial robot (UR5e) to insert pegs of different shapes and sizes, without further training.