Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance

Abstract

Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

“…In addition, Lee et al (2020) observed significantly higher effective connectivity from the supplementary motor area (SMA) to the right dorsolateral prefrontal cortex (DLPFC) in high aptitude BCI users during resting-state when compared to low aptitude performers. Implementing these findings, research showed that functional connectivity during the MI task (Yi et al, 2014;Stefano Filho et al, 2017;Gu et al, 2020;Vidaurre et al, 2020;etc. ) or the change in functional connectivity from resting-state to MI task can be used as a feature for MI-BCI classification (Gonuguntla et al, 2016;Hamedi et al, 2016).…”

“…In the former studies, both effective connectivity (e.g., Lee et al, 2020) and functional connectivity (e.g., Vidaurre et al, 2020) measures have been investigated, however, these studies employed various metrics of connectivity including coherence, phase synchronization, phase-slope index, etc., which employ different algorithms and hence vary in their interpretation (Bastos and Schoffelen, 2016). However, to fully tackle the disadvantages of EEG, such as artifacts and inter-trial/inter-subject amplitude variability, phase-based relationships (e.g., phase synchronization) might provide the best functional connectivity measure of spatially distributed regions that are active during mental task execution (Caicedo-Acosta et al, 2021).…”

“…Some studies computed connectivity as average synchronization between distributed brain regions i.e., an average of all electrode connections over the scalp (e.g., Zhang et al, 2015). Others make the distinction between local scale and large scale (e.g., Wang et al, 2006;Zhang et al, 2014;Vidaurre et al, 2020). In addition, some researchers worked with source localization algorithms before applying connectivity measures (e.g., Gu et al, 2020;Lee et al, 2020;Vidaurre et al, 2020).…”

Harmoni: a Method for Eliminating Spurious Interactions due to the Harmonic Components in Neuronal Data

“…This has been recognized earlier as a major challenge for studying phase-amplitude coupling (PAC) in neuronal data (Aru et al, 2015;Giehl et al, 2021;Jensen et al, 2016;Lozano-Soldevilla et al, 2016;Zhang et al, 2021) as well as for n:m phase-synchronization (Hyafil, 2017;Scheffer-Teixeira and Tort, 2016;Siebenhühner et al, 2020). In this work, we directly addressed the issue of spurious interactions due to waveshape of oscillations and offer a solution for the assessment of phase synchronization as one of the most important measures used for connectivity analyses with brain electrophysiology (Marzetti et al, 2019;Nentwich et al, Sadaghiani et al, 2021;Vidaurre et al, 2020). Currently available measures for quantifying n:m phase-synchronization (also referred to as cross-frequency synchronization -CFS) are not suitable for differentiation between genuine and spurious interactions.…”

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

“…A solid reason for BCI illiteracy is that subjects with poor control performance do not exhibit discriminative task-related changes over the modulation of Sensorimotor Rhythms (SMR) within the resting-state as well as in the interval of MI responses [11]. In particular, BCI faces real-world challenges, which are mostly caused by the low spatial resolution of EEG that, along with the nonstationarity present in the recorded neurophysiological signals, results in a poor signal-to-noise ratio (SNR).…”