ArticleJournal of neural engineering2019
High-frequency band temporal dynamics in response to a grasp force task.
Article in Journal of neural engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 13 citations in OpenAlex.
- Signal properties and stability of a chronically implanted endovascular brain computer interface.medRxiv : the preprint server for health sciences · 2025Article
- Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBrain-computer interfaces (BCIs) are being developed to restore reach and grasping movements of paralyzed individuals. Recent studies have shown that the kinetics of grasping movement, such as grasp force, can be successfully decoded from electrocorticography (ECoG) signals, and that the high-frequency band (HFB) power changes provide discriminative information that contribute to an accurate decoding of grasp force profiles. However, as the models used in these studies contained simultaneous information from multiple spectral features over multiple areas in the brain, it remains unclear what parameters of movement and force are encoded by the HFB signals and how these are represented temporally and spatially in the SMC. APPROACH: To investigate this, and to gain insight in the temporal dynamics of the HFB during grasping, we continuously modelled the ECoG HFB response recorded from nine individuals with epilepsy temporarily implanted with ECoG grids, who performed three different grasp force tasks. MAIN
resultsWe show that a model based on the force onset and offset consistently provides a better fit to the HFB power responses when compared with a model based on the force magnitude, irrespective of electrode location. SIGNIFICANCE: Our results suggest that HFB power, although potentially useful for continuous decoding, is more closely related to the changes in movement. This finding may potentially contribute to the more natural decoding of grasping movement in neural prosthetics.
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