{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n=============================================\nInterpolate bad channels for MEG/EEG channels\n=============================================\n\nThis example shows how to interpolate bad MEG/EEG channels\n\n - Using spherical splines as described in [1]_ for EEG data.\n - Using field interpolation for MEG data.\n\nThe bad channels will still be marked as bad. Only the data in those channels\nis removed.\n\nReferences\n----------\n.. [1] Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989)\n Spherical splines for scalp potential and current density mapping.\n Electroencephalography and Clinical Neurophysiology, Feb; 72(2):184-7.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Authors: Denis A. Engemann \n# Mainak Jas \n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nfname = data_path + '/MEG/sample/sample_audvis-ave.fif'\nevoked = mne.read_evokeds(fname, condition='Left Auditory',\n baseline=(None, 0))\n\n# plot with bads\nevoked.plot(exclude=[])\n\n# compute interpolation (also works with Raw and Epochs objects)\nevoked.interpolate_bads(reset_bads=False)\n\n# plot interpolated (previous bads)\nevoked.plot(exclude=[])" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.13", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }