Dimensionality reduction for EMG prediction of upper-limb activity in freely-behaving primates
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Date
28/11/2013Author
Krasoulis, Agamemnon
Metadata
Abstract
Neural prosthetic systems aim to assist patients suffering from sensory, motor and
other disabilities by translating neural brain activity into control signals for assistive
devices, such as computers and robotics prostheses, or by restoring muscle contraction
through functional electrical stimulation (FES). In a neuro-motor prosthetic device, the
prediction of intended muscle activity is required for effective FES. It has been already
known that upper-limb electromyogram (EMG) signals in primates, can be accurately
predicted during repetitive tasks, by decoding the spiking-activity (SA) of single cells
and multi-unit activity (MUA) in the motor cortical areas. Recent work now suggests
that EMG signals can also be decoded by local field potential (LFP) recordings from
the same areas. In such decoding schemes, the number of input variables is usually
very large and no systematic way of performing effective variable selection has yet
been suggested. In this work, we demonstrated for the first time that muscle activity
decoding from SA and LFP signals in the primary motor cortex (M1) and the ventral
premotor cortex (PMv) areas is feasible during naturalistic free behaviour, and we
compared the decoding performance of spike-, LFP- and hybrid decoders. We also
tested the relative information in a number of LFP frequency bands, and found that
mid-high and high-frequency bands (70 - 244 Hz) conveyed the most EMG-related
information. Finally, we compared the decoding performance of a group of sparse
regression algorithms, and we showed that a method based on the variational bayes
(VB) outperformed the conventional Wiener cascade filter for LFP-decoders in the
case of limited amount of training data. For longer training datasets, the results from
all methods were comparable.