Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex
Year: 2017
Type: article
Abstract: Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.
Source: Scientific Reports
DOI: 10.1038/srep45486
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Brain machine interfaces to restore swallowing function, G2975605253, Development of a decoding method for imagined characters (vowels and consonants) using EEG., Development of decoding method to infer visual imagery using ECoG signals, Power assist and clinical application that could predict and control viscoelasticity for rehabilitation of stroke, G5190683306, Neural basis of mental images, G967371729
OpenAlex ID: W2599514851
