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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

ents Cereb 660 Cortex 15: 131-140, 2005 661 662 Alpaydin E. Introduction to machine learning. Cambridge, MA:M1T, 2004, p. 254 663 664 Ball T, Schulze-Bonhage A, Aertsen A, Mebring C. Differential representation of arm 665 movement direction in relation to cortical anatomy and function J Neural Eng 6: 016

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

. Mu rhythm (del synchronization and EEG single-trial classilkation of different motor imagery tasks. Neuroimage 31.153-159. Pistohl. T. Ball. T.. Schulze-Bonhage. A.. Aensen.A. Mehsing, C., 2008. Prediction of arm movement trajectories from ECoG-recordings in humans. J. Neuroses. Methods 167. 105-114. Ricke

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

ents Cereb 660 Cortex 15: 131-140, 2005 661 662 Alpaydin E. Introduction to machine learning. Cambridge, MA:MIT, 2004, p. 254 663 664 Ball T, Schulze-Bonhage A, Aertsen A, Mehring C. Differential representation of arm 665 movement direction in relation to cortical anatomy and function J Neural Eng 6: 016