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al. 2003). Signals from each EEG electrode were decimated by a 149 factor of 5 (to 100 liz), then filtered with a zero-phase, 3rd order, band-pass Butterworth filter 150 (0.1 — 2 Hz) and normalized by subtracting their mean and dividing by their standard deviation 151 (Bradbeny et al. 2010). Kinematic d

796 797 Tallon-Baudr) C. Bertrand O, Delpuech C. Pernier J. Oscillatory y-Band (30-70 Hz) activity 798 induced by a visual search task in humans. J. Neurosci 17(2): 722-734, 1997 799 800 Tononi G, Riedner BA, Hulse BK, Ferrarelli F, Sarasso S. Enhancing sleep slow waves with 801 natural stimuli. Medic

mployed in this study has been previously described (Bradberry et a! 2010) so will only briefly be described here. First, a fourth-order, low-pass Butterworth filter with a cutoff frequency of I Hz was applied to the kinematic and EEG data. Very low frequencies have previously been shown to possess kinema

berry Ti, Gentili R J and Contreras-Vidal J L 2010 Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals J. Neurosci. 30 31:2 7 Bradherry Ti, Rong F and Contreras-Vidal J L 2009 Decoding center-out hand velocity from MEG signals during visuomotor adaptation Neur

The best decoding results were obtained when both the MEG and kinematic data were subsequently filtered with a zero-phase. fourth-order, low-pass Butterworth filter with a cutoff frequency of 15 Hz. The data for each phase of the task were pre-processed separately. Decoding model In the subsequent analy

Delorme. A.. Matteis, 5.. 2004. EEGLAB: an open source toolbox for analysis of single. trial EEC dynamics including independent component analysis. J. Neurosci. Methods 134.9-21. Fuchs. M.. Kastner. J. Wagner. M. Hawn. S.. Ebersole, JS. 2002. A standardized boundary element method volume conductor model.