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.4.2 software. Additionally the EEG signals were band-pass filtered from 0.5 to 100 Hz and notch filter
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
ity 121 were recorded (sampling rate of 500 Hz; band-pass filtered from 0.1 to 100 Hz; right ear lobe
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
v43 software. Additionally, the EEG signals were band- pass filtered from 0.01 to 30 Hz. Electroocular
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
white noise region. On-line. electronic circuits band-pass and notch-filtered the MEG data from 1-100 H
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
ity 121 were recorded (sampling rate of 500 Hz; band-pass filtered from 0.1 to 100 Hz; right ear lobe
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
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