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The Journal of Neuroscience, January 23, 2008, 28(4):1000-1008; doi:10.1523/JNEUROSCI.5171-07.2008

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Behavioral/Systems/Cognitive
Hand Movement Direction Decoded from MEG and EEG

Stephan Waldert,^1,2,3 Hubert Preissl,^1,5 Evariste Demandt,² Christoph Braun,¹ Niels Birbaumer,¹ Ad Aertsen,^3,4 and Carsten Mehring^2,3

¹Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany, ²Institute of Biology I, ³Bernstein Center for Computational Neuroscience, and ⁴Institute of Biology III, Albert-Ludwigs-University, 79104 Freiburg, Germany, and ⁵Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205

Correspondence should be addressed to Stephan Waldert, Institute of Biology I, University of Freiburg, Hauptstrasse 1, 79104 Freiburg, Germany. Email: waldert@bccn.uni-freiburg.de

Brain activity can be used as a control signal for brain–machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for 7 Hz (low-frequency band) and 62–87 Hz (high- band) and a decrease for 10–30 Hz (β band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the β and high- bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.

Key words: MEG; EEG; BMI; decoding; hand movement; motor cortex

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Received Aug. 29, 2007; accepted Dec. 13, 2007.

Correspondence should be addressed to Stephan Waldert, Institute of Biology I, University of Freiburg, Hauptstrasse 1, 79104 Freiburg, Germany. Email: waldert@bccn.uni-freiburg.de

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