Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain‐Computer Interface perspective

Abstract

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

“…However, the presence of tonic elements agrees with intracortical studies (Smith et al, 1975;Wannier et al, 1991), which demonstrated both tonic and dynamic neural responses to executed forces; and fMRI studies (Branco et al, 2019), which demonstrated a monotonic relationship between the BOLD response and static force magnitudes. Moreover, despite the presence of dynamic response elements, offline force classification performance remained relatively stable throughout the go phase ( Figures 6, 6-1), suggesting that the tonic elements could allow for adequate real-time force decoding using linear techniques alone.…”

“…Second, force-tuned neurons in motor cortex respond more to the direction of applied force rather its magnitude (Kalaska and Hyde, 1985;Kalaska et al, 1989;Taira et al, 1996). Finally, intracortical non-human primate studies (Georgopoulos et al, 1983;Georgopoulos et al, 1992) and fMRI studies in humans (Branco et al, 2019) suggest that motor cortical neurons respond more to the dynamics of force than to static force tasks. The present work, which recorded from rostral motor cortex and studied the representation of static, non-directional forces, may therefore have detected weaker force-related representation than would have been possible from more caudally-placed recording arrays during a dynamic, functional force task.…”

“…In contrast, the second group suggests that force, grasp, and other motor parameters interact within the neural space (Hepp-Reymond et al, 1999;Degenhart et al, 2011). They propose that motor parameters cannot be fully de-coupled (Kalaska, 2009;Branco et al, 2019), and that it may be more effective to utilize the entire motor output to develop a comprehensive mechanical model, rather than trying to extract single parameters such as force and grasp ).…”

“…Moreover, despite the presence of dynamic response elements, offline force classification performance remained relatively stable throughout the go phase ( Figures 6, 6-1), suggesting that the tonic elements could allow for adequate real-time force decoding using linear techniques alone. This may be especially true when decoding forces during dynamic functional tasks, which have been shown to elicit stronger, more consistent neural responses within motor cortex (Georgopoulos et al, 1983;Georgopoulos et al, 1992;Branco et al, 2019).…”

“…Early work by Evarts and others, which showed correlations between cortical activity and force output (Evarts, 1968;Humphrey, 1970;Fetz and Cheney, 1980;Evarts et al, 1983;Kalaska et al, 1989), and later work, which directly decoded muscle activations from neurons in primary motor cortex (M1) (Morrow and Miller, 2003;Sergio and Kalaska, 2003;Pohlmeyer et al, 2007;Oby et al, 2010), suggest that cortex encodes low-level dynamics of movement along with kinematics (Kakei et al, 1999;Carmena et al, 2003;Branco et al, 2019). However, explorations of kinetic information as a control signal for iBCIs have only just begun.…”