Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy

Reinmar Kobler, Andreea Ioana Sburlea, Valeria Mondini, Masayuki Hirata, Gernot Müller-Putz

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: One of the main goals in brain-computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. APPROACH: In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. MAIN RESULTS: At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. SIGNIFICANCE: We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.

Original languageEnglish
Number of pages1
JournalJournal of Neural Engineering
Volume17
Issue number5
DOIs
Publication statusPublished - 4 Nov 2020

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy'. Together they form a unique fingerprint.

Cite this