Epstein Secrets

lying a low-pass anti-aliasing filter with a cutoff frequency of 40 Hz and then downsampling by a factor of 10. A zero-phase, fourth-order, low-pass Butterworth filter with a cutoff frequency of 1 Hz was then applied to the kinematic and EEG data. The cutoff frequency was determined experimentally with infl

s of sensor contributions were produced by the topoplot function of EEGLAB, an open-source MATLAB toolbox for electrophysiological data processing (Delorme and Makeig, 2004; http://sccn.ucsd.edu/eeglab/), that performs biharmonic spline interpolation of the sensor values before plotting them (Sandwell,

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

773 774 Nielsen JB. How we walk: central control of muscle activity during human walking 775 Neuroscientist 9: 195-204, 2003 776 777 Onton J, Delorme A, Makeig S. Frontal midline EEG dynamics during working memory 778 Neurolmage 27: 341-356, 2005 779 780 Pfurtscheller G, Brunner C, Schlogl, Lo

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

-110-0 ms in increments of 10 ms) of scalp maps. These spatial renderings of sensor contributions were produced by the topoplot function of EEGLAB (Delorme and Makcig 2004), an open-source MATLAB toolbox for electrophysiological data processing that performs bihannonic spline interpolation (Sandwell 1

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

eras-Vidal. J.L Kerick. S.E.. 2004. Independent component analysis of dynamic brain responses during visuornotor adaptation. Neuroimage 21.936-945. Delorme. A.. Matteis, 5.. 2004. EEGLAB: an open source toolbox for analysis of single. trial EEC dynamics including independent component analysis. J. Neuro

t al. 2003). Signals from each EEG electrode were decimated by a 149 factor of 5 (to 100 Hz), 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 (Bradberry et al. 2010). Kinematic

773 774 Nielsen JB. How we walk: central control of muscle activity during human walking 775 Neuroscientist 9: 195-204, 2003 776 777 Onton J, Delorme A, Makeig S. Frontal midline EEG dynamics during working memory 778 Neurolmage 27: 341-356, 2005 779 780 Pfurtscheller G, Brunner C, Schlogl, Lo