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Inference of hand movements from local field potentials in monkey motor cortex

An Erratum to this article was published on 01 January 2004

Abstract

The spiking of neuronal populations in motor cortex provides accurate information about movement parameters. Here we show that hand movement target and velocity can be inferred from multiple local field potentials (LFPs) in single trials approximately as efficiently as from multiple single-unit activity (SUA) recorded from the same electrodes. Our results indicate that LFPs can be used as an additional signal for decoding brain activity, particularly for new neuroprosthetic applications.

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Figure 1: Example of LFP, SUA and MUA tuning, averaged over 20 trials per target.
Figure 2: Decoding of movement target from individual SUAs, MUAs and LFPs.
Figure 3: Decoding of movement target and trajectories from multielectrode recordings.

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Acknowledgements

This study was supported by the Boehringer Ingelheim Fonds (BIF), the German-Israeli Foundation for Scientific Research and Development (GIF), the German Bundesministerium für Bildung und Forschung (BMBF-DIP), the WIN-Kolleg of the Heidelberg Academy of Sciences and Humanities (Germany), the Deutsche Forschungsgemeinschaft (DFG, CA 245) and the ISF center of excellence grant (8006/00).

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Correspondence to Carsten Mehring.

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Supplementary information

Supplementary Fig. 1.

Temporal evolution of tuning strength. Colors depict the different types of signals: LFPs (green), SUAs which were checked for stability (red) and MUAs (blue). Tuning strength was calculated as the signal-to-noise ratio of the tuning curves in windows of 50 ms width from the unsmoothed signals. (JPG 14 kb)

Supplementary Fig. 2.

Decoding power and accuracy of trajectory prediction for different algorithms. We used the neuronal signals (LFP, SUA, MUA) that were recorded simultaneously by eight electrodes. The graphs depict averages over 10 recording sessions and both, left- and right-handed movements. In (a), (b) and (c) results are shown for the whole recording set of single-units. Subfigure (b) includes results for SUAs additionally checked for stability across trials. (a) Decoding power for different classification algorithms: PLDA, SVM with radial basis function kernel, SVM with linear kernel, multivariate Gaussian model, population vector approach (from left to right). Error bars depict the standard deviation. (b) Decoding power as a function of the width of the smoothing kernel. Colors and line styles as in Fig. 3a. (c) Accuracy of 2D trajectory prediction (average correlation coefficients across sessions and handedness) using either SVM regression (SVM) or the linear filter (LF). (JPG 10 kb)

Supplementary Fig. 3.

Decoding power of individual single-units taken from the whole recording set regardless of stability. (a) Distribution of decoding power for individual SUA, shown for contralateral (left) and ipsilateral (right) movements. Dotted lines depict the chance level (0.125). (b) Contra- and ipsilateral decoding power was largely uncorrelated. (JPG 15 kb)

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Mehring, C., Rickert, J., Vaadia, E. et al. Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci 6, 1253–1254 (2003). https://doi.org/10.1038/nn1158

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