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Journal of Physiology-Paris
Volume 103, Issues 3-5, September 2009, Pages 244-254
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Copyright © 2009 Elsevier Ltd All rights reserved.

A review on directional information in neural signals for brain-machine interfaces
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Stephan Walderta, b, Corresponding Author Contact Information, 1, E-mail The Corresponding Author, Tobias Pistohla, b, 1, E-mail The Corresponding Author, Christoph Braunc, d, E-mail The Corresponding Author, Tonio Balla, b, e, E-mail The Corresponding Author, Ad Aertsena, b, E-mail The Corresponding Author and Carsten Mehringa, b, Corresponding Author Contact Information, E-mail The Corresponding Author

aFaculty of Biology, Albert-Ludwigs-University, Hauptstr. 1, 79104 Freiburg, Germany

bBernstein Center for Computational Neuroscience, Hansastr. 9a, 79104 Freiburg, Germany

cMEG-Center, University of Tübingen, Tübingen, Germany

dCIMeC, Center of Mind/Brain Sciences, University of Trento, Trento, Italy

eEpilepsy Center, University Clinics, Albert-Ludwigs University, Freiburg, Germany

Available online 7 August 2009.


Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.

Keywords: Directional tuning; Decoding; SUA; MUA; LFP; ECoG; EEG; MEG; BMI; BCI

Article Outline

1. Introduction
1.1. Brain-machine interfaces
1.2. Different BMI approaches
1.2.1. Recording techniques
1.2.2. Mental task or strategy
1.2.3. Direct motor BMI
2. Directional tuning
2.1. Directional tuning in spiking signals: SUA and MUA
2.2. Amplitude spectrograms of analog population signals
2.3. Directional tuning in analog population signals: LFP, ECoG, EEG, MEG
3. Movement decoding
3.1. Movement decoding using analog population signals: movement direction
3.2. Movement decoding using analog population signals: movement trajectories
4. Discussion
4.1. Relevance of low-pass filtered analog neural population signals
4.2. Directional tuning of signals reflecting a wide range of spatial scales: from SUA to EEG/MEG
4.3. Online direct motor BMI and adaptivity
Appendix A
A.1. Deduction of the expected amplitude of a cosine created by the sum of cosines with random phases
A.2. Deduction of the noise amplitude resulting from summation of noise

Corresponding Author Contact InformationCorresponding authors. Address: Faculty of Biology, Hauptstr. 1, 79104 Freiburg, Germany. Tel.: +49 761 203 2542; fax: +49 761 203 2921 (S. Walbert), tel.: +49 761 203 2543; fax: +49 761 203 2921 (C. Mehring).
1 These authors contributed equally to the manuscript.
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Journal of Physiology-Paris
Volume 103, Issues 3-5, September 2009, Pages 244-254
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