.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/SSVEP/plot_classify_ssvep_pga.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_SSVEP_plot_classify_ssvep_pga.py: =============================================================================== Visualization of SSVEP-based BCI Classification in Tangent Space =============================================================================== Project extended covariance matrices of SSVEP-based BCI in the tangent space, using principal geodesic analysis (PGA). You should have a look to "Offline SSVEP-based BCI Multiclass Prediction" before this example. .. GENERATED FROM PYTHON SOURCE LINES 12-31 .. code-block:: Python # Authors: Quentin Barthélemy, Emmanuel Kalunga and Sylvain Chevallier # # License: BSD (3-clause) import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from mne import find_events, Epochs, make_fixed_length_epochs from mne.io import Raw from sklearn.pipeline import make_pipeline from sklearn.decomposition import PCA from pyriemann.estimation import BlockCovariances from pyriemann.classification import MDM from pyriemann.tangentspace import TangentSpace from pyriemann.utils.viz import _add_alpha from helpers.ssvep_helpers import download_data, extend_signal .. GENERATED FROM PYTHON SOURCE LINES 32-50 .. code-block:: Python clabel = ['resting-state', '13 Hz', '17 Hz', '21 Hz'] clist = plt.cm.viridis(np.array([0, 1, 2, 3])/3) cmap = "viridis" def plot_pga(ax, data, labels, centers): sc = ax.scatter(data[:, 0], data[:, 1], c=labels, marker='P', cmap=cmap) ax.scatter( centers[:, 0], centers[:, 1], c=clist, marker='o', s=100, cmap=cmap ) ax.set(xlabel='PGA, 1st axis', ylabel='PGA, 2nd axis') for i in range(len(clabel)): ax.scatter([], [], color=clist[i], marker='o', s=50, label=clabel[i]) ax.legend(loc='upper right') return sc .. GENERATED FROM PYTHON SOURCE LINES 51-53 Load EEG and extract covariance matrices for SSVEP -------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 53-89 .. code-block:: Python frequencies = [13, 17, 21] freq_band = 0.1 events_id = {'13 Hz': 2, '17 Hz': 4, '21 Hz': 3, 'resting-state': 1} duration = 2.5 # duration of epochs interval = 0.25 # interval between successive epochs for online processing # Subject 12: first 4 sessions for training, last session for test # Training set raw = Raw(download_data(subject=12, session=1), preload=True, verbose=False) events = find_events(raw, shortest_event=0, verbose=False) raw = raw.pick("eeg") ch_count = len(raw.info['ch_names']) raw_ext = extend_signal(raw, frequencies, freq_band) epochs = Epochs( raw_ext, events, events_id, tmin=2, tmax=5, baseline=None, verbose=False ).get_data(copy=False) x_train = BlockCovariances( estimator='lwf', block_size=ch_count ).transform(epochs) y_train = events[:, 2] # Testing set raw = Raw(download_data(subject=12, session=4), preload=True, verbose=False) raw = raw.pick_types(eeg=True) raw_ext = extend_signal(raw, frequencies, freq_band) epochs = make_fixed_length_epochs( raw_ext, duration=duration, overlap=duration - interval, verbose=False ).get_data(copy=False) x_test = BlockCovariances( estimator='lwf', block_size=ch_count ).transform(epochs) .. rst-class:: sphx-glr-script-out .. code-block:: none Download complete in 00s (3.2 MB) Creating RawArray with float64 data, n_channels=24, n_times=92384 Range : 0 ... 92383 = 0.000 ... 360.871 secs Ready. Using data from preloaded Raw for 32 events and 769 original time points ... 0 bad epochs dropped 0%| | 0.00/5.35M [00:00 
MDM(metric={'distance': 'riemann', 'mean': 'riemann'})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 102-108 Projection in tangent space with principal geodesic analysis (PGA) ------------------------------------------------------------------ Project covariance matrices from the Riemannian manifold into the Euclidean tangent space at the grand average, and apply a principal component analysis (PCA) to obtain an unsupervised dimension reduction [1]_. .. GENERATED FROM PYTHON SOURCE LINES 108-118 .. code-block:: Python pga = make_pipeline( TangentSpace(metric="riemann", tsupdate=False), PCA(n_components=2) ) ts_train = pga.fit_transform(x_train) ts_means = pga.transform(np.array(mdm.covmeans_)) .. GENERATED FROM PYTHON SOURCE LINES 119-128 Offline training of MDM visualized by PGA ----------------------------------------- These figures show the trajectory on the tangent space taken by covariance matrices during a 4-class SSVEP experiment, and how they are classified epoch by epoch. This figure reproduces Fig 3(c) of reference [2]_, showing training trials of best subject. .. GENERATED FROM PYTHON SOURCE LINES 128-135 .. code-block:: Python fig, ax = plt.subplots(figsize=(8, 8)) fig.suptitle('PGA of training set', fontsize=16) plot_pga(ax, ts_train, y_train, ts_means) plt.show() .. image-sg:: /auto_examples/SSVEP/images/sphx_glr_plot_classify_ssvep_pga_001.png :alt: PGA of training set :srcset: /auto_examples/SSVEP/images/sphx_glr_plot_classify_ssvep_pga_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pyriemann/checkouts/latest/examples/SSVEP/plot_classify_ssvep_pga.py:40: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored ax.scatter( .. GENERATED FROM PYTHON SOURCE LINES 136-144 Online classification by MDM visualized by PGA ---------------------------------------------- This figure reproduces Fig 6 of reference [2]_, with an animation to imitate an online acquisition, processing and classification of EEG time-series. Warning: [2]_ uses a curved based online classification, while a single trial classification is used here. .. GENERATED FROM PYTHON SOURCE LINES 144-157 .. code-block:: Python # Prepare data for online classification test_visu = 50 # nb of matrices to display simultaneously colors, ts_visu = [], np.empty([0, 2]) alphas = np.linspace(0, 1, test_visu) fig, ax = plt.subplots(figsize=(8, 8)) fig.suptitle('PGA of testing set', fontsize=16) pl = plot_pga(ax, ts_visu, colors, ts_means) pl.axes.set_xlim(-5, 6) pl.axes.set_ylim(-5, 5) .. image-sg:: /auto_examples/SSVEP/images/sphx_glr_plot_classify_ssvep_pga_002.png :alt: PGA of testing set :srcset: /auto_examples/SSVEP/images/sphx_glr_plot_classify_ssvep_pga_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-5.0, 5.0) .. GENERATED FROM PYTHON SOURCE LINES 158-189 .. code-block:: Python # Prepare animation for online classification def online_classify(t): global colors, ts_visu # Online classification y = mdm.predict(x_test[np.newaxis, t]) color = clist[int(y[0] - 1)] ts_test = pga.transform(x_test[np.newaxis, t]) # Update data colors.append(color) ts_visu = np.vstack((ts_visu, ts_test)) if len(ts_visu) > test_visu: colors.pop(0) ts_visu = ts_visu[1:] colors = _add_alpha(colors, alphas) # Update plot pl.set_offsets(np.c_[ts_visu[:, 0], ts_visu[:, 1]]) pl.set_color(colors) return pl interval_display = 1.0 # can be changed for a slower display visu = FuncAnimation(fig, online_classify, frames=range(0, len(x_test)), interval=interval_display, blit=False, repeat=False) .. GENERATED FROM PYTHON SOURCE LINES 190-206 .. code-block:: Python # Plot online classification # Plot complete visu: a dynamic display is required plt.show() # Plot only 10s, for animated documentation try: from IPython.display import HTML except ImportError: raise ImportError("Install IPython to plot animation in documentation") plt.rcParams["animation.embed_limit"] = 10 HTML(visu.to_jshtml(fps=5, default_mode='loop')) .. rst-class:: sphx-glr-script-out .. code-block:: none Animation size has reached 10525072 bytes, exceeding the limit of 10485760.0. If you're sure you want a larger animation embedded, set the animation.embed_limit rc parameter to a larger value (in MB). This and further frames will be dropped. .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 207-218 References ---------- .. [1] `Principal geodesic analysis for the study of nonlinear statistics of shape `_ P.T. Fletcher, C. Lu, S. M. Pizer, S. Joshi. IEEE Transactions on Medical Imaging (Volume: 23, Issue: 8, August 2004). .. [2] `Online SSVEP-based BCI using Riemannian geometry `_ E. K. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli, Y. Hamam. Neurocomputing, Elsevier, 2016, 191, pp.55-68. .. rst-class:: sphx-glr-timing **Total running time of the script:** (4 minutes 59.550 seconds) .. _sphx_glr_download_auto_examples_SSVEP_plot_classify_ssvep_pga.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_classify_ssvep_pga.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classify_ssvep_pga.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_