A review on directional information in neural signals for brain-machine interfaces |
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Authors: | Stephan Waldert Tobias Pistohl Christoph Braun Tonio Ball Ad Aertsen Carsten Mehring |
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Institution: | 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 |
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Abstract: | 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. |
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Keywords: | Directional tuning Decoding SUA MUA LFP ECoG EEG MEG BMI BCI |
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