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Examples Gallery
================

.. contents:: Contents
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   :depth: 2



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Classification of ERP
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Using Riemannian geometry for classifying event-related potentials (ERP).



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    <div class="sphx-glr-thumbcontainer" tooltip="Riemannian embeddings via Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE) of a set ...">

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  .. image:: /auto_examples/ERP/images/thumb/sphx_glr_plot_embedding_MEG_thumb.png
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  :ref:`sphx_glr_auto_examples_ERP_plot_embedding_MEG.py`

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      <div class="sphx-glr-thumbnail-title">Embedding ERP MEG data in 2D Euclidean space</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Different ways to display a multichannel event-related potential (ERP).">

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  .. image:: /auto_examples/ERP/images/thumb/sphx_glr_plot_ERP_thumb.png
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  :ref:`sphx_glr_auto_examples_ERP_plot_ERP.py`

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      <div class="sphx-glr-thumbnail-title">Display ERP</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Decoding applied to EEG data in sensor space decomposed using Xdawn. After spatial filtering, c...">

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  .. image:: /auto_examples/ERP/images/thumb/sphx_glr_plot_classify_EEG_tangentspace_thumb.png
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  :ref:`sphx_glr_auto_examples_ERP_plot_classify_EEG_tangentspace.py`

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      <div class="sphx-glr-thumbnail-title">ERP EEG decoding in Tangent space.</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Decoding applied to MEG data in sensor space decomposed using Xdawn. After spatial filtering, c...">

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  .. image:: /auto_examples/ERP/images/thumb/sphx_glr_plot_classify_MEG_mdm_thumb.png
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  :ref:`sphx_glr_auto_examples_ERP_plot_classify_MEG_mdm.py`

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      <div class="sphx-glr-thumbnail-title">Multiclass MEG ERP Decoding</div>
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Classification of SSVEP
-----------------------

Using Riemannian geometry for classifying steady-state visually evoked potentials (SSVEP).



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    <div class="sphx-glr-thumbcontainer" tooltip="Building extended covariance matrices for SSVEP-based BCI. The obtained matrices are shown. A M...">

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  .. image:: /auto_examples/SSVEP/images/thumb/sphx_glr_plot_classify_ssvep_mdm_thumb.png
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  :ref:`sphx_glr_auto_examples_SSVEP_plot_classify_ssvep_mdm.py`

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      <div class="sphx-glr-thumbnail-title">Offline SSVEP-based BCI Multiclass Prediction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Project extended covariance matrices of SSVEP-based BCI in the tangent space, using principal g...">

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  .. image:: /auto_examples/SSVEP/images/thumb/sphx_glr_plot_classify_ssvep_pga_thumb.png
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  :ref:`sphx_glr_auto_examples_SSVEP_plot_classify_ssvep_pga.py`

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      <div class="sphx-glr-thumbnail-title">Visualization of SSVEP-based BCI Classification in Tangent Space</div>
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Artifact management
-------------------

Using Riemannian geometry to detect, reject or correct artifacts.



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    <div class="sphx-glr-thumbcontainer" tooltip="Blind source separation (BSS) based on approximate joint diagonalization of Fourier cospectra (...">

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  .. image:: /auto_examples/artifacts/images/thumb/sphx_glr_plot_correct_ajdc_EEG_thumb.png
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  :ref:`sphx_glr_auto_examples_artifacts_plot_correct_ajdc_EEG.py`

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      <div class="sphx-glr-thumbnail-title">Artifact Correction by AJDC-based Blind Source Separation</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Example of Riemannian Potato [1]_ applied on EEG time-series to detect artifacts in online proc...">

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  .. image:: /auto_examples/artifacts/images/thumb/sphx_glr_plot_detect_riemannian_potato_EEG_thumb.png
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  :ref:`sphx_glr_auto_examples_artifacts_plot_detect_riemannian_potato_EEG.py`

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      <div class="sphx-glr-thumbnail-title">Online Artifact Detection with Riemannian Potato</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Example of Riemannian Potato Field (RPF) [1]_ applied on EEG time-series to detect artifacts in...">

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  .. image:: /auto_examples/artifacts/images/thumb/sphx_glr_plot_detect_riemannian_potato_field_EEG_thumb.png
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  :ref:`sphx_glr_auto_examples_artifacts_plot_detect_riemannian_potato_field_EEG.py`

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      <div class="sphx-glr-thumbnail-title">Online Artifact Detection with Riemannian Potato Field</div>
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Classification of motor imagery
-------------------------------

Using Riemannian geometry for classifying motor imagery.



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    <div class="sphx-glr-thumbcontainer" tooltip="Classify motor imagery data with Riemannian geometry [1]_.">

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  .. image:: /auto_examples/motor-imagery/images/thumb/sphx_glr_plot_single_thumb.png
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  :ref:`sphx_glr_auto_examples_motor-imagery_plot_single.py`

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      <div class="sphx-glr-thumbnail-title">Motor imagery classification</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to compute SPD matrices from functional connectivity estimators and how ...">

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  .. image:: /auto_examples/motor-imagery/images/thumb/sphx_glr_plot_ensemble_coherence_thumb.png
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  :ref:`sphx_glr_auto_examples_motor-imagery_plot_ensemble_coherence.py`

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      <div class="sphx-glr-thumbnail-title">Ensemble learning on functional connectivity</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Find optimal frequency band using class distinctiveness measure on the manifold and compare cla...">

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  .. image:: /auto_examples/motor-imagery/images/thumb/sphx_glr_plot_frequency_band_selection_thumb.png
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  :ref:`sphx_glr_auto_examples_motor-imagery_plot_frequency_band_selection.py`

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      <div class="sphx-glr-thumbnail-title">Frequency band selection on the manifold for motor imagery classification</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example compares classification pipelines based on covariance maxtrix (CM) versus augmente...">

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  .. image:: /auto_examples/motor-imagery/images/thumb/sphx_glr_plot_augmented_thumb.png
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  :ref:`sphx_glr_auto_examples_motor-imagery_plot_augmented.py`

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      <div class="sphx-glr-thumbnail-title">Augmented Covariance Matrix</div>
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Covariance estimation
---------------------

Examples for covariance matrix estimation.



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    <div class="sphx-glr-thumbcontainer" tooltip="Comparison of robustness of different covariance estimators on a corrupted low-dimensional data...">

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  .. image:: /auto_examples/signal/images/thumb/sphx_glr_plot_covariance_estimation_robust_thumb.png
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  :ref:`sphx_glr_auto_examples_signal_plot_covariance_estimation_robust.py`

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      <div class="sphx-glr-thumbnail-title">Robust covariance estimation</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Comparison of covariance estimators for different EEG signal lengths and their impact on classi...">

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  .. image:: /auto_examples/signal/images/thumb/sphx_glr_plot_covariance_estimation_thumb.png
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  :ref:`sphx_glr_auto_examples_signal_plot_covariance_estimation.py`

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      <div class="sphx-glr-thumbnail-title">Compare covariance and kernel estimators</div>
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Simulated data
---------------

Examples using datasets sampled from known probability distributions.



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    <div class="sphx-glr-thumbcontainer" tooltip="Spectral embedding of samples from the Riemannian Gaussian distribution with different centerin...">

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  .. image:: /auto_examples/simulated/images/thumb/sphx_glr_plot_riemannian_gaussian_thumb.png
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  :ref:`sphx_glr_auto_examples_simulated_plot_riemannian_gaussian.py`

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      <div class="sphx-glr-thumbnail-title">Sample from the Riemannian Gaussian distribution in the SPD manifold</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Generate several datasets containing data points from two-classes. Each class is generated with...">

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  .. image:: /auto_examples/simulated/images/thumb/sphx_glr_plot_toy_classification_thumb.png
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  :ref:`sphx_glr_auto_examples_simulated_plot_toy_classification.py`

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      <div class="sphx-glr-thumbnail-title">Classification accuracy vs class distinctiveness vs class separability</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="A comparison between Euclidean and Riemannian means [1]_, and Euclidean and Riemannian geometri...">

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  .. image:: /auto_examples/simulated/images/thumb/sphx_glr_plot_mean_median_comparison_thumb.png
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  :ref:`sphx_glr_auto_examples_simulated_plot_mean_median_comparison.py`

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      <div class="sphx-glr-thumbnail-title">Mean and median comparison</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Estimate the mean of SPD matrices corrupted by NaN values [1]_.">

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  .. image:: /auto_examples/simulated/images/thumb/sphx_glr_plot_nanmean_thumb.png
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  :ref:`sphx_glr_auto_examples_simulated_plot_nanmean.py`

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      <div class="sphx-glr-thumbnail-title">Estimate mean of SPD matrices with NaN values</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers on low-dimensional synthetic datasets, adapted to SPD matri...">

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  .. image:: /auto_examples/simulated/images/thumb/sphx_glr_plot_classifier_comparison_thumb.png
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  :ref:`sphx_glr_auto_examples_simulated_plot_classifier_comparison.py`

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      <div class="sphx-glr-thumbnail-title">Classifier comparison</div>
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Permutation test
----------------

Permutation test with pyRiemann.



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    <div class="sphx-glr-thumbcontainer" tooltip="One way manova to compare Left vs Right in time.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_plot_oneWay_Manova_time_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_plot_oneWay_Manova_time.py`

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      <div class="sphx-glr-thumbnail-title">One Way manova time</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="One way manova to compare Left vs Right for each frequency.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_plot_oneWay_Manova_frequency_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_plot_oneWay_Manova_frequency.py`

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      <div class="sphx-glr-thumbnail-title">One Way manova with Frequenty</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="One way manova to compare Left vs Right.">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_plot_oneWay_Manova_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_plot_oneWay_Manova.py`

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      <div class="sphx-glr-thumbnail-title">One Way manova</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Manova for ERP data">

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  .. image:: /auto_examples/stats/images/thumb/sphx_glr_oneWay_Manova_ERP_thumb.png
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  :ref:`sphx_glr_auto_examples_stats_oneWay_Manova_ERP.py`

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      <div class="sphx-glr-thumbnail-title">Manova for ERP data</div>
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Transfer learning
-----------------

Using Riemannian geometry for transfer learning and domain adaptation.



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    <div class="sphx-glr-thumbcontainer" tooltip="Use the SpectralEmbedding module to plot in 2D the transformations on the data points from sour...">

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  .. image:: /auto_examples/transfer/images/thumb/sphx_glr_plot_rpa_steps_thumb.png
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  :ref:`sphx_glr_auto_examples_transfer_plot_rpa_steps.py`

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      <div class="sphx-glr-thumbnail-title">Plot the data transformations in the Riemannian Procrustes Analysis</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we use transfer learning (TL) to classify epochs from a subject using a classi...">

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  .. image:: /auto_examples/transfer/images/thumb/sphx_glr_plot_bci_example_thumb.png
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  :ref:`sphx_glr_auto_examples_transfer_plot_bci_example.py`

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      <div class="sphx-glr-thumbnail-title">Motor imagery classification by transfer learning</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="We compare the classification performance of MDM on five different strategies for transfer lear...">

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  .. image:: /auto_examples/transfer/images/thumb/sphx_glr_plot_comparison_pipelines_thumb.png
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  :ref:`sphx_glr_auto_examples_transfer_plot_comparison_pipelines.py`

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      <div class="sphx-glr-thumbnail-title">Comparison of pipelines for transfer learning</div>
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.. toctree::
   :hidden:
   :includehidden:


   /auto_examples/ERP/index.rst
   /auto_examples/SSVEP/index.rst
   /auto_examples/artifacts/index.rst
   /auto_examples/motor-imagery/index.rst
   /auto_examples/signal/index.rst
   /auto_examples/simulated/index.rst
   /auto_examples/stats/index.rst
   /auto_examples/transfer/index.rst


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      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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