Dataset Information

Implicit Grasp Force Representation in Human Motor Cortical Recordings.

ABSTRACT: In order for brain-computer interface (BCI) systems to maximize functionality, users will need to be able to accurately modulate grasp force to avoid dropping heavy objects while also being able to handle fragile items. We present a case-study consisting of two experiments designed to identify whether intracortical recordings from the motor cortex of a person with tetraplegia could predict intended grasp force. In the first task, we were able classify neural responses to attempted grasps of four objects, each of which required similar grasp kinematics but different implicit grasp force targets, with 69% accuracy. In the second task, the subject attempted to move a virtual robotic arm in space to grasp a simple virtual object. For each trial, the subject was asked to grasp the virtual object with the force appropriate for one of the four objects from the first experiment, with the goal of measuring an implicit representation of grasp force. While the subject knew the grasp force during all phases of the trial, accurate classification was only achieved during active grasping, not while the hand moved to, transported, or released the object. In both tasks, misclassifications were most often to the object with an adjacent force requirement. In addition to the implications for understanding the representation of grasp force in motor cortex, these results are a first step toward creating intelligent algorithms to help BCI users grasp and manipulate a variety of objects that will be encountered in daily life. Clinical Trial Identifier: NCT01894802 https://clinicaltrials.gov/ct2/show/NCT01894802.

SUBMITTER: Downey JE

PROVIDER: S-EPMC6220062 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Implicit Grasp Force Representation in Human Motor Cortical Recordings.

Downey John E JE Weiss Jeffrey M JM Flesher Sharlene N SN Thumser Zachary C ZC Marasco Paul D PD Boninger Michael L ML Gaunt Robert A RA Collinger Jennifer L JL

Frontiers in neuroscience 20181031

In order for brain-computer interface (BCI) systems to maximize functionality, users will need to be able to accurately modulate grasp force to avoid dropping heavy objects while also being able to handle fragile items. We present a case-study consisting of two experiments designed to identify whether intracortical recordings from the motor cortex of a person with tetraplegia could predict intended grasp force. In the first task, we were able classify neural responses to attempted grasps of four ...[more]

PMID: 30429772

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Project description:Interactions between the ventral premotor (PMv) and the primary motor cortex (M1) are crucial for transforming an object's geometrical properties, such as its size and shape, into a motor command suitable for grasp of the object. Recently, we showed that PMv interacts with M1 in a specific fashion, depending on the hand posture. However, the functional connectivity between PMv and M1 during the preparation of an actual grasp is still unknown. To address this issue, PMv-M1 interactions were tested while subjects were preparing to grasp different visible objects requiring either a precision grip or a whole hand grasp. A conditioning-test transcranial magnetic stimulation (TMS) paradigm was used: a test stimulus was applied over M1 either in isolation or after a conditioning stimulus delivered, at different delays, over the ipsilateral PMv. Motor evoked potentials (MEPs) were recorded in the first dorsal interosseus and abductor digiti minimi muscles, which show highly differentiated activity according to grasp. While subjects prepared to grasp, delivering a conditioning PMv pulse 6 or 8msec before a test pulse over M1 strikingly facilitated MEPs in the specific muscles that were used in the upcoming grasp. This degree of facilitation correlated with the amount of muscle activity used later in the trial to grasp the objects. The present results demonstrate that, during grasp preparation, the PMv-M1 interactions are muscle-specific. PMv appears to process the object geometrical properties relevant for the upcoming grasp, and transmits this information to M1, which in turn generates a motor command appropriate for the grasp. We also reveal that the grasp-specific facilitation resulting from PMv-M1 interactions is differently related to the upcoming grasp muscle activity than is that from paired-pulse stimulation over M1, suggesting that these two TMS paradigms assess the excitability of cortico-cortical pathways devoted to the control of grasp at two different levels.

Dataset Information

Implicit Grasp Force Representation in Human Motor Cortical Recordings.

Publications

Implicit Grasp Force Representation in Human Motor Cortical Recordings.

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