Abstract
Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.
Highlights
A number of prominent brain-machine interface studies have arisen, in which electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and intracortical microelectrode have been applied to neuroprosthesis control, neurorehabilitation and novel communication tools for paralyzed or ‘‘locked–in’’ patients suffering from neuromuscular disorders
The predicted red lines fit the peaks at various timings, even though the ECoG signals utilized for the prediction were common between q2 and q3
We predicted 3D arm trajectory in humans based on ECoG signals divided into seven frequency bands using a sparse linear regression method
Summary
A number of prominent brain-machine interface studies have arisen, in which electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and intracortical microelectrode have been applied to neuroprosthesis control, neurorehabilitation and novel communication tools for paralyzed or ‘‘locked–in’’ patients suffering from neuromuscular disorders. Using motor cortical signals in animals, studies have shown successful prediction of hand trajectories [14,15,16] and grasp types and velocity [17], control of a computer cursor [18] or a robot arm [19,20,21,22], and controlled stimulation to a paralyzed arm [23]. These techniques have been applied in humans to control a cursor [24] and a virtual keyboard and virtual hand [25]. Though intracortical electrodes can provide rich information for BMI control, they face limitations such as signal degradation due to glial scarring [26]and potential displacement from the recording site [27]
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