Predicting Movement from Multiunit Activity

By Eran Stark, Moshe Abeles. Journal of Neuroscience, 2007

317 citations recorded by the source corpus.

Previous studies have shown that intracortical activity can be used to operate prosthetic devices such as an artificial limb. Previously used neuronal signals were either the activity of tens to hundreds of spiking neurons, which are difficult to record for long periods of time, or local field potentials, which are highly correlated with each other. Here, we show that by estimating multiunit activity (MUA), the superimposed activity of many neurons around a microelectrode, and using a small number of electrodes, an accurate prediction of the upcoming movement is obtained. Compared with single-unit spikes, single MUA recordings are obtained more easily and the recordings are more stable over time. Compared with local field potentials, pairs of MUA recordings are considerably less redundant. Compared with any other intracortical signal, single MUA recordings are more informative. MUA is informative even in the absence of spikes. By combining information from multielectrode recordings from the motor cortices of monkeys that performed either discrete prehension or continuous tracing movements, we demonstrate that predictions based on multichannel MUA are superior to those based on either spikes or local field potentials. These results demonstrate that considerable information is retained in the superimposed activity of multiple neurons, and therefore suggest that neurons within the same locality process similar information. They also illustrate that complex movements can be predicted using relatively simple signal processing without the detection of spikes and, thus, hold the potential to greatly expedite the development of motor-cortical prosthetic devices.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a87839772f52b833fc585b42fedf8e72b6583309

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

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Citations in the corpus

Brain-machine interfaces in neurorehabilitation of stroke. (2015, unknown): “Such signals are most effectively obtained using invasive recordings with intracortical, subdural or epidural electrodes (Mehring et al., 2004; Stark and Abeles, 2007; Zhuang et al., 2010; Slutzky et al., 2010, 2011; Flint et al., 2014).”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, methodology): “However, with present algorithms, spike sorting leads to very little improvement in BMI performance [12, 31–33].”