Local field potentials in primate motor cortex encode grasp kinetic parameters

Abstract

Reach and grasp kinematics are known to be encoded in the spiking activity of neuronal ensembles and in local field potentials (LFPs) recorded from primate motor cortex during movement planning and execution. However, little is known, especially in LFPs, about the encoding of kinetic parameters, such as forces exerted on the object during the same actions. We implanted two monkeys with microelectrode arrays in the motor cortical areas MI and PMd to investigate encoding of grasp-related parameters in motor cortical LFPs during planning and execution of reach-and-grasp movements. We identified three components of the LFP that modulated during grasps corresponding to low (0.3 - 7Hz), intermediate (∼10 - ∼40Hz) and high (∼80 - 250Hz) frequency bands. We show that all three components can be used to classify not only grip types but also object loads during planning and execution of a grasping movement. In addition, we demonstrate that all three components recorded during planning or execution can be used to continuously decode finger pressure forces and hand position related to the grasping movement. Low and high frequency components provide similar classification and decoding accuracies, which were substantially higher than those obtained from the intermediate frequency component. Our results demonstrate that intended reach and grasp kinetic parameters are encoded in multiple LFP bands during both movement planning and execution. These findings also suggest that the LFP is a reliable signal for the control of parameters related to object load and applied pressure forces in brain-machine interfaces.

Cortical control of object‐specific grasp relies on adjustments of both activity and effective connectivity: a common marmoset study

“…However, we showed a number of differences between epochs in sensorimotor cortex, for a subset of grips -namely finger and scissor grips -associated with small objects. To our knowledge, no identical results have been reported in macaques or humans, although it is well-known that gamma oscillations encode movement kinematics Mollazadeh et al 2008Mollazadeh et al , 2011Kubanek et al 2009;Pistohl et al 2012;Milekovic et al 2015) that varies across task epochs (Roy et al 2000;Castiello, 2005;Takemi et al 2014). Our results are compatible with single-unit studies pointing out that populations of neurons show different patterns of activity depending on grip type and task epoch (Umiltà et al 2007).…”

“…; Milekovic et al . ). However, intermediate frequencies (10–45 Hz) are known to provide smaller classification and decoding accuracy than low (<7 Hz) and high (75–250 Hz) frequencies (Schalk et al .…”

“…; Milekovic et al . ). In fact, the issue of beta power modulation by movement parameters is still controversial (Kilavik et al .…”

“…There is evidence that substantial information on grasping features, including kinematics and forces, is contained in LFPs (Pistohl et al 2012;Milekovic et al 2015). However, intermediate frequencies (10-45 Hz) are known to provide smaller classification and decoding accuracy than low (<7 Hz) and high (75-250 Hz) frequencies Mollazadeh et al 2008Mollazadeh et al , 2011Kubanek et al 2009;Pistohl et al 2012;Milekovic et al 2015). In fact, the issue of beta power modulation by movement parameters is still controversial .…”

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

“…[11]), or machine learning methods for classification (e.g. [3], [5], [16]). Yet, since communication in the brain is performed via discrete events, so-called spikes, it is intuitive to use the same protocol in a realistic closedloop brain-computer interfacing system.…”