Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm.

Journal of Neural Engineering
Valeria MondiniGernot R Müller-Putz

Abstract

Continuous decoding of voluntary movement is desirable for closed-loop, natural control of neuroprostheses. Recent studies showed the possibility to reconstruct the hand trajectories from low-frequency (LF) electroencephalographic (EEG) signals. So far this has only been performed offline. Here, we attempt for the first time continuous online control of a robotic arm with LF-EEG-based decoded movements. The study involved ten healthy participants, asked to track a moving target by controlling a robotic arm. At the beginning of the experiment, the robot was fully controlled by the participant's hand trajectories. After calibrating the decoding model, that control was gradually replaced by LF-EEG-based decoded trajectories, first with 33%, 66% and finally 100% EEG control. Likewise with other offline studies, we regressed the movement parameters (two-dimensional positions, velocities, and accelerations) from the EEG with partial least squares (PLS) regression. To integrate the information from the different movement parameters, we introduced a combined PLS and Kalman filtering approach (named PLSKF). We obtained moderate yet overall significant (α = 0.05) online correlations between hand kinematics and PLSKF-decoded trajectories ...Continue Reading

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Oct 1, 2021·International Journal of Neural Systems·Emma ColamarinoJlenia Toppi

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