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

Reinmar J Kobler, Andreea I Sburlea, Valeria Mondini, Masayuki Hirata, Gernot R Müller-Putz*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)


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
Article number056027
Number of pages21
JournalJournal of Neural Engineering
Issue number5
Publication statusPublished - 4-Nov-2020

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