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

Valeria Mondini, Reinmar Josef Kobler, Andreea Ioana Sburlea, Gernot R Müller-Putz*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: 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.

APPROACH: 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).

MAIN RESULTS: We obtained moderate yet overall significant (α = 0.05) online correlations between hand kinematics and PLSKF-decoded trajectories of 0.32 on average. With respect to PLS regression alone, the PLSKF had a stable correlation increase of Δr = 0.049 on average, demonstrating the successful integration of different models. Parieto-occipital activations were highlighted for the velocity and acceleration decoder patterns. The level of robot control was above chance in all conditions. Participants finally reported to feel enough control to be able to improve with training, even in the 100% EEG condition.

SIGNIFICANCE: Continuous low frequency EEG-based movement decoding for the online control of a robotic arm was achieved for the first time. The potential bottlenecks arising when switching from offline to online decoding, and possible solutions, were described. The effect of the PLSKF and its extensibility to different experimental designs were discussed.

Original languageEnglish
Article number046031
Number of pages18
JournalJournal of Neural Engineering
Volume17
Issue number4
Early online date17 Jul 2020
DOIs
Publication statusPublished - 11 Aug 2020

Fields of Expertise

  • Human- & Biotechnology

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