.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ERP/plot_ERP.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ERP_plot_ERP.py: =============================================================================== Display ERP =============================================================================== Different ways to display a multichannel event-related potential (ERP). .. GENERATED FROM PYTHON SOURCE LINES 9-19 .. code-block:: Python # Authors: Quentin Barthélemy # # License: BSD (3-clause) import numpy as np import mne from matplotlib import pyplot as plt from pyriemann.utils.viz import plot_waveforms .. GENERATED FROM PYTHON SOURCE LINES 20-22 Load EEG data ------------- .. GENERATED FROM PYTHON SOURCE LINES 22-48 .. code-block:: Python # Set filenames data_path = str(mne.datasets.sample.data_path()) raw_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw.fif" event_fname = data_path + "/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif" # Read raw data, select occipital channels and high-pass filter signal raw = mne.io.Raw(raw_fname, preload=True, verbose=False) raw.pick_channels(['EEG 057', 'EEG 058', 'EEG 059'], ordered=True) raw.rename_channels({'EEG 057': 'O1', 'EEG 058': 'Oz', 'EEG 059': 'O2'}) n_channels = len(raw.ch_names) raw.filter(1.0, None, method="iir") # Read epochs and get responses to left visual field stimulus tmin, tmax = -0.1, 0.8 epochs = mne.Epochs( raw, mne.read_events(event_fname), {'vis_l': 3}, tmin, tmax, proj=False, baseline=None, preload=True, verbose=False) X = 5e5 * epochs.get_data(copy=False) print('Number of trials:', X.shape[0]) times = np.linspace(tmin, tmax, num=X.shape[2]) plt.rcParams["figure.figsize"] = (7, 12) ylims = [] .. rst-class:: sphx-glr-script-out .. code-block:: none NOTE: pick_channels() is a legacy function. New code should use inst.pick(...). Removing projector Removing projector Removing projector Filtering raw data in 1 contiguous segment Setting up high-pass filter at 1 Hz IIR filter parameters --------------------- Butterworth highpass zero-phase (two-pass forward and reverse) non-causal filter: - Filter order 8 (effective, after forward-backward) - Cutoff at 1.00 Hz: -6.02 dB Number of trials: 73 .. GENERATED FROM PYTHON SOURCE LINES 49-53 Plot all trials --------------- This kind of plot is a little bit messy. .. GENERATED FROM PYTHON SOURCE LINES 53-64 .. code-block:: Python fig = plot_waveforms(X, 'all', times=times, alpha=0.3) fig.suptitle('Plot all trials', fontsize=16) for i_channel in range(n_channels): fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel]) fig.axes[i_channel].set_xlim(tmin, tmax) ylims.append(fig.axes[i_channel].get_ylim()) fig.axes[n_channels - 1].set(xlabel='Time') plt.show() .. image-sg:: /auto_examples/ERP/images/sphx_glr_plot_ERP_001.png :alt: Plot all trials :srcset: /auto_examples/ERP/images/sphx_glr_plot_ERP_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 65-71 Plot central tendency and dispersion of trials ---------------------------------------------- This kind of plot is well-spread, but mean and standard deviation can be contaminated by artifacts, and they make a symmetric assumption on amplitude distribution. .. GENERATED FROM PYTHON SOURCE LINES 71-82 .. code-block:: Python fig = plot_waveforms(X, 'mean+/-std', times=times) fig.suptitle('Plot mean+/-std of trials', fontsize=16) for i_channel in range(n_channels): fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel]) fig.axes[i_channel].set_xlim(tmin, tmax) fig.axes[i_channel].set_ylim(ylims[i_channel]) fig.axes[n_channels - 1].set(xlabel='Time') plt.show() .. image-sg:: /auto_examples/ERP/images/sphx_glr_plot_ERP_002.png :alt: Plot mean+/-std of trials :srcset: /auto_examples/ERP/images/sphx_glr_plot_ERP_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 83-87 Plot histogram of trials ------------------------ This plot estimates a 2D histogram of trials [1]_. .. GENERATED FROM PYTHON SOURCE LINES 87-97 .. code-block:: Python fig = plot_waveforms(X, 'hist', times=times, n_bins=25, cmap=plt.cm.Greys) fig.suptitle('Plot histogram of trials', fontsize=16) for i_channel in range(n_channels): fig.axes[i_channel].set(ylabel=raw.ch_names[i_channel]) fig.axes[i_channel].set_ylim(ylims[i_channel]) fig.axes[n_channels - 1].set(xlabel='Time') plt.show() .. image-sg:: /auto_examples/ERP/images/sphx_glr_plot_ERP_003.png :alt: Plot histogram of trials :srcset: /auto_examples/ERP/images/sphx_glr_plot_ERP_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 98-105 References ---------- .. [1] `Improved estimation of EEG evoked potentials by jitter compensation and enhancing spatial filters `_ A. Souloumiac and B. Rivet. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.283 seconds) .. _sphx_glr_download_auto_examples_ERP_plot_ERP.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ERP.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ERP.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_