Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals

Abstract

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

“…Motor variability due to variability in human kinematic parameters, e.g., force field adaptation, speed and trajectory, and motivational factors such as level of user engagement, arousal and feelings of competence, necessary for performing a motor task is an integral part of the motor learning process (Duarte and Reinkensmeyer, 2015;Úbeda et al, 2015;Edelman et al, 2019;Faller et al, 2019). Such variability does not necessarily represent noise contents only, but may potentially be a manifestation of motor and perceptual learning processes.…”

“…Individuals with higher motor variability may learn a skill faster than individuals with lower motor variability (Wu et al, 2014;Singh et al, 2016). The EEG patterns associated with motor variability could therefore partly explain intra-individual variability in SMR-based BCI (Bradberry et al, 2010;Úbeda et al, 2015;Ostry and Gribble, 2016). Furthermore, structural and functional differences between subjects are associated with motor learning process, which might explain the motor learning variability (Tomassini et al, 2011).…”

Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques

“…Recent works suggest that it is possible to decode hand or arm kinematics (position and velocity) from slow cortical potentials (SCPs), i.e., EEG signals oscillations below 2 Hz [ 17 – 20 ]. To that end, multidimensional linear regression models are applied to the data.…”

Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

“…Previous works used short processing time windows (i.e., 100 ms) in the application of linear decoders to SCPs, (Bradberry et al, 2010 ; Presacco et al, 2011 ; Úbeda et al, 2015 ). By using this approach, it is possible to obtain significant performance (in terms of signal-to-signal correlation) in the decoding of upper and lower limb kinematics.…”

“…In these studies, kinematic parameters were directly decoded from the activity of larger regions of the scalp by applying linear decoders to SCPs for decoding both upper and lower limb joint movements (Bradberry et al, 2010 ; Presacco et al, 2011 ), sitting and standing states (Bulea et al, 2014 ), finger movements (Paek et al, 2014 ), and types of grasping (Agashe et al, 2015 ). Other studies have dealt with the characteristics of the performed movement, showing that hand kinematics are better decoded when continuous and linear movements are performed (Úbeda et al, 2015 ) and exploring the possibility of using them to classify reaching directions (Úbeda et al, 2017 ).…”