.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/dss/plot_01_dss_fundamentals.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_dss_plot_01_dss_fundamentals.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_dss_plot_01_dss_fundamentals.py:


Fundamentals of DSS.
=====================

This tutorial introduces **Denoising Source Separation (DSS)**, a technique for
extracting brain sources based on a specific criterion of "interestingness" (bias).

Unlike PCA, which finds components of high *variance*, or ICA, which finds components
of high *non-Gaussianity*, DSS finds components that maximize a user-defined **Bias**.

The core optimization is:

.. math::
   \max_w \frac{w^T R_{biased} w}{w^T R_{baseline} w}

where :math:`R_{biased}` is the covariance of the signal of interest and
:math:`R_{baseline}` is the covariance of the raw data or noise.

This allows DSS to be extremely flexible. The "Bias" defines what you are looking for.
Typical biases include trial averaging for stimulus-evoked responses, bandpass
filtering for oscillatory sources, and time masking for artifact removal.

This tutorial demonstrates the "Hello World" of DSS: extracting a repetitive signal
buried in noise using the **Trial Average Bias**.

Authors: Sina Esmaeili (sina.esmaeili@umontreal.ca)
         Hamza Abdelhedi (hamza.abdelhedi@umontreal.ca)

.. GENERATED FROM PYTHON SOURCE LINES 31-33

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 33-50

.. code-block:: Python

    import contextlib
    import os

    import mne
    import numpy as np
    from mne.datasets import sample

    from mne_denoise.dss import DSS, AverageBias, BandpassBias
    from mne_denoise.viz import (
        plot_component_patterns,
        plot_component_score_curve,
        plot_component_summary,
        plot_component_time_series,
        plot_evoked_gfp_comparison,
        plot_psd_comparison,
    )








.. GENERATED FROM PYTHON SOURCE LINES 51-58

Part 1: Synthetic Data
----------------------
We generate synthetic data with distinct components to demonstrate different biases.
Signal A (Evoked): 10 Hz sine wave, phase-locked (reproducible).
Signal B (Oscillatory): 50 Hz, random phase
(not reproducible, but distinct frequency).
Noise: White noise.

.. GENERATED FROM PYTHON SOURCE LINES 58-135

.. code-block:: Python


    print("Generating synthetic data...")
    n_epochs = 50
    n_times = 500
    n_channels = 32
    sfreq = 250

    times = np.arange(n_times) / sfreq
    data = np.zeros((n_epochs, n_channels, n_times))

    # Create standard montage for realistic topomaps
    montage = mne.channels.make_standard_montage("standard_1020")
    ch_names = montage.ch_names[:n_channels]
    info = mne.create_info(ch_names, sfreq, "eeg")
    info.set_montage(montage)

    # Generate smooth spatial patterns (dipole-like)
    # This fixes the "noisy topomap" issue by ensuring
    # adjacent sensors have similar weights.
    pos = np.array([ch["loc"][:3] for ch in info["chs"]])
    center_head = np.mean(pos, axis=0)

    # Pattern 1: Left-ish
    target_pos_1 = center_head + np.array([-0.05, 0, 0])
    dists_1 = np.linalg.norm(pos - target_pos_1, axis=1)
    mixing_evoked = np.exp(-(dists_1**2) / 0.02)
    mixing_evoked /= np.linalg.norm(mixing_evoked)

    # Pattern 2: Right-ish
    target_pos_2 = center_head + np.array([0.05, 0, 0.05])
    dists_2 = np.linalg.norm(pos - target_pos_2, axis=1)
    mixing_osc = np.exp(-(dists_2**2) / 0.02)
    mixing_osc /= np.linalg.norm(mixing_osc)

    rng = np.random.default_rng(42)

    # Generate spatially correlated noise (Background Activity)
    # Random dipoles to ensure noise has smooth topography (like real brain data)
    n_noise_sources = 20
    noise_mix = np.zeros((n_channels, n_noise_sources))
    for k in range(n_noise_sources):
        # Random target position
        rand_pos = center_head + rng.uniform(-0.06, 0.06, 3)
        dists = np.linalg.norm(pos - rand_pos, axis=1)
        # Smooth spatial field
        field = np.exp(-(dists**2) / 0.015)
        noise_mix[:, k] = field / np.linalg.norm(field)

    for i in range(n_epochs):
        # 1. Evoked Signal (10 Hz, reproducible)
        signal_evoked = np.sin(2 * np.pi * 10 * times) * 2.0

        # 2. Oscillatory interference (50 Hz, random phase)
        # We also give this a smooth topography (Oscillator pattern)
        phase = rng.uniform(0, 2 * np.pi)
        signal_osc = np.sin(2 * np.pi * 50 * times + phase) * 1.5

        # 3. Background Brain Noise (Spatially Smooth)
        noise_src = rng.standard_normal((n_noise_sources, n_times))
        brain_noise = noise_mix @ noise_src * 0.5

        # 4. Sensor Noise (White, small)
        sensor_noise = rng.standard_normal((n_channels, n_times)) * 0.1

        # Combine
        data[i] = (
            np.outer(mixing_evoked, signal_evoked)
            + np.outer(mixing_osc, signal_osc)
            + brain_noise
            + sensor_noise
        )

    epochs = mne.EpochsArray(data, info)
    epochs_picks = np.arange(len(epochs.ch_names))
    print(f"Created epochs: {epochs.get_data().shape}")






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Generating synthetic data...
    Not setting metadata
    50 matching events found
    No baseline correction applied
    0 projection items activated
    Created epochs: (50, 32, 500)




.. GENERATED FROM PYTHON SOURCE LINES 136-140

Synthetic A: Trial Average Bias
-------------------------------
Goal: Isolate the **Evoked (10Hz)** component.
Bias: Maximize power of the mean over epochs.

.. GENERATED FROM PYTHON SOURCE LINES 140-155

.. code-block:: Python


    print("\n--- Synthetic: Trial Average Bias ---")
    dss_evoked = DSS(n_components=3, bias=AverageBias(), return_type="sources")
    dss_evoked.fit(epochs)

    # Visualize
    # Score Curve
    # -----------
    # This plot shows the "Bias Ratio" for each component.
    # Expectation: The first component (Comp 0) should have a much
    # higher score than the rest.
    # This indicates that Comp 0 is highly reproducible (signal),
    # while others are noise.
    plot_component_score_curve(dss_evoked, mode="ratio", show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_001.png
   :alt: Component Scores
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    --- Synthetic: Trial Average Bias ---
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:496: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      baseline_cov = mne.compute_covariance(inst, method=method, **kws)
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:498: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      biased_cov = mne.compute_covariance(biased_inst, method=method, **kws)

    <Figure size 1400x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 156-161

Component Time Series
---------------------
We view the time courses of the first 5 components.
Expectation: Comp 0 should look like a clean 10 Hz sine wave.
Expectation: Comp 1-4 should look like noise or the 50 Hz interference.

.. GENERATED FROM PYTHON SOURCE LINES 161-163

.. code-block:: Python

    plot_component_time_series(dss_evoked, data=epochs, n_components=3, show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_002.png
   :alt: Component Time Series
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2000x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 164-176

Spatial Patterns
----------------
The "Spatial Pattern" (or topomap) shows how the component maps onto the sensors.

Interpretation:
Colors: Red/Blue indicate opposite polarity. Strong colors mean the component
is strongly present on those sensors.
Dots: These represent the 32 electrodes of the 'standard_1020' montage.
Comp 0: Shows a smooth dipolar field (the "Left-ish" pattern we simulated).
Comp 1+: Often look "speckled" or messy, indicating they capture noise.

Note: Since the data is synthetic, the sensor locations are idealized.

.. GENERATED FROM PYTHON SOURCE LINES 176-184

.. code-block:: Python

    plot_component_patterns(
        dss_evoked,
        info=epochs.info,
        picks=epochs_picks,
        n_components=3,
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_003.png
   :alt: Component Patterns, Comp 0, Comp 1, Comp 2
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_003.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 1800x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 185-188

Component Summary
-----------------
A dashboard for detailed inspection of Comp 0.

.. GENERATED FROM PYTHON SOURCE LINES 188-197

.. code-block:: Python

    plot_component_summary(
        dss_evoked,
        data=epochs,
        info=epochs.info,
        picks=epochs_picks,
        n_components=[0],
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_004.png
   :alt: Comp 0 Pattern, Comp 0 Time Course, PSD
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_004.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 198-202

Denoising Comparison
--------------------
We reconstruct the data using ONLY the first component (the "Signal").
This removes the 50Hz interference and white noise.

.. GENERATED FROM PYTHON SOURCE LINES 202-215

.. code-block:: Python

    print("Reconstructing data from first component...")
    sources = dss_evoked.transform(epochs)
    # To reconstruct using only specific components, we zero out the others
    sources[:, 1:, :] = 0
    epochs_denoised = dss_evoked.inverse_transform(sources)
    epochs_denoised = mne.EpochsArray(epochs_denoised, info)

    # Plot Original vs Denoised Evoked Response
    # Expectation: The "Denoised" trace should have smaller confidence intervals
    # because the variable noise has been removed.
    plot_evoked_gfp_comparison(epochs, epochs_denoised, times=epochs.times, show=True)





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_005.png
   :alt: Evoked GFP Comparison
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_005.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Reconstructing data from first component...
    Not setting metadata
    50 matching events found
    No baseline correction applied
    0 projection items activated

    <Figure size 2000x1200 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 216-222

Synthetic B: Bandpass Bias
--------------------------
Goal: Isolate the **Oscillatory (50Hz)** component.
Note: This component cancels out in the trial average!
But DSS can find it by maximizing 50Hz power.
Bias: Maximize power in 48-52 Hz band.

.. GENERATED FROM PYTHON SOURCE LINES 222-234

.. code-block:: Python


    print("\n--- Synthetic: Bandpass Bias (50Hz) ---")
    bias_bp = BandpassBias(freq_band=(48, 52), sfreq=sfreq)
    dss_osc = DSS(n_components=5, bias=bias_bp)
    # For Bandpass, we often treat data as continuous (Raw),
    # but Epochs work too (concatenated).
    dss_osc.fit(epochs)

    # Visualize
    # Score Curve
    plot_component_score_curve(dss_osc, mode="ratio", show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_006.png
   :alt: Component Scores
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_006.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    --- Synthetic: Bandpass Bias (50Hz) ---
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:496: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      baseline_cov = mne.compute_covariance(inst, method=method, **kws)
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:498: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      biased_cov = mne.compute_covariance(biased_inst, method=method, **kws)

    <Figure size 1400x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 235-238

Component Time Series
Expectation: Comp 0 should look like a bursty/clean 50Hz oscillation.
Note: Unlike Evoked, these are not phase-locked, so peaks don't align across trials.

.. GENERATED FROM PYTHON SOURCE LINES 238-240

.. code-block:: Python

    plot_component_time_series(dss_osc, data=epochs, n_components=3, show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_007.png
   :alt: Component Time Series
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_007.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2000x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 241-245

Spatial Patterns
Expectation: Comp 0 should show the "Right-ish" field pattern.
Note: This topography is distinct from the Evoked signal,
showing how DSS separates sources spatially.

.. GENERATED FROM PYTHON SOURCE LINES 245-253

.. code-block:: Python

    plot_component_patterns(
        dss_osc,
        info=epochs.info,
        picks=epochs_picks,
        n_components=3,
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_008.png
   :alt: Component Patterns, Comp 0, Comp 1, Comp 2
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_008.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 1800x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 254-256

Component Summary
Expectation: PSD should show a very sharp peak at 50 Hz.

.. GENERATED FROM PYTHON SOURCE LINES 256-265

.. code-block:: Python

    plot_component_summary(
        dss_osc,
        data=epochs,
        info=epochs.info,
        picks=epochs_picks,
        n_components=[0],
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_009.png
   :alt: Comp 0 Pattern, Comp 0 Time Course, PSD
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_009.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 266-269

Denoising Comparison
--------------------
Reconstruct data using the oscillator component.

.. GENERATED FROM PYTHON SOURCE LINES 269-283

.. code-block:: Python

    print("Reconstructing data from oscillatory component...")
    # We concatenate epochs for continuous reconstruction if desired, or keep as epochs
    # Here we keep as epochs to use plot_psd_comparison
    sources = dss_osc.transform(epochs)
    sources[:, 1:, :] = 0
    epochs_osc = dss_osc.inverse_transform(sources)
    epochs_osc = mne.EpochsArray(epochs_osc, info)

    # Plot PSD Comparison
    # Expectation: The "Denoised" signal should have a massive peak at 50 Hz
    # and very little power elsewhere (noise suppressed).
    plot_psd_comparison(epochs, epochs_osc, show=True, fmax=100)





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_010.png
   :alt: PSD Comparison
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_010.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Reconstructing data from oscillatory component...
    Not setting metadata
    50 matching events found
    No baseline correction applied
    0 projection items activated
        Using multitaper spectrum estimation with 7 DPSS windows
        Using multitaper spectrum estimation with 7 DPSS windows

    <Figure size 1600x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 284-289

Part 2: Real Data (MNE Sample)
------------------------------
We load real MEG data and perform the same two tasks as above: recover the
auditory evoked response with a trial-average bias and recover background
alpha rhythm with a bandpass bias.

.. GENERATED FROM PYTHON SOURCE LINES 289-307

.. code-block:: Python


    print("\nLoading MNE Sample data...")
    # Ensure MNE_DATA directory exists
    home = os.path.expanduser("~")
    mne_data_path = os.path.join(home, "mne_data")
    if not os.path.exists(mne_data_path):
        with contextlib.suppress(OSError):
            os.makedirs(mne_data_path)

    data_path = sample.data_path()
    raw_fname = data_path / "MEG" / "sample" / "sample_audvis_raw.fif"
    event_fname = data_path / "MEG" / "sample" / "sample_audvis_raw-eve.fif"

    raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose=False)
    raw.pick_types(meg="grad", eeg=False, eog=False, stim=False).crop(0, 60)
    raw_picks = np.arange(len(raw.ch_names))
    print(f"Data: {len(raw.ch_names)} Gradiometers, 60s duration")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Loading MNE Sample data...
    Using default location ~/mne_data for sample...
    Fetching 1 file for the sample dataset ...
    Downloading file 'MNE-sample-data-processed.tar.gz' from 'https://osf.io/download/86qa2?version=6' to '/tmp/mne-denoise-home/mne_data'.

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    Untarring contents of '/tmp/mne-denoise-home/mne_data/MNE-sample-data-processed.tar.gz' to '/tmp/mne-denoise-home/mne_data'
    Attempting to create new mne-python configuration file:
    /tmp/tmpjuhgh_kt/.mne/mne-python.json
    Could not read the /tmp/tmpjuhgh_kt/.mne/mne-python.json json file during the writing. Assuming it is empty. Got: Expecting value: line 1 column 1 (char 0)
    Download complete in 49s (1576.2 MB)
    NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
    Data: 203 Gradiometers, 60s duration




.. GENERATED FROM PYTHON SOURCE LINES 308-311

Real A: Bandpass Bias (Alpha Rhythm)
------------------------------------
Goal: Find Alpha (8-12 Hz) components.

.. GENERATED FROM PYTHON SOURCE LINES 311-321

.. code-block:: Python


    print("\n--- Real: Bandpass Bias (Alpha) ---")
    bias_alpha = BandpassBias(freq_band=(8, 12), sfreq=raw.info["sfreq"])
    dss_alpha = DSS(n_components=5, bias=bias_alpha)
    dss_alpha.fit(raw)

    # Visualize
    # Score Curve
    plot_component_score_curve(dss_alpha, mode="ratio", show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_011.png
   :alt: Component Scores
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_011.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    --- Real: Bandpass Bias (Alpha) ---

    <Figure size 1400x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 322-324

Component Time Series
Expectation: Strong rhythmic activity (alpha waves) in the first component.

.. GENERATED FROM PYTHON SOURCE LINES 324-326

.. code-block:: Python

    plot_component_time_series(dss_alpha, data=raw, n_components=5, show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_012.png
   :alt: Component Time Series
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_012.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2000x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 327-330

Spatial Patterns
Expectation: Comp 0 shows a posterior/occipital topography (visual/alpha areas).
Note: The dots here represent the MEG sensors (gradiometers).

.. GENERATED FROM PYTHON SOURCE LINES 330-338

.. code-block:: Python

    plot_component_patterns(
        dss_alpha,
        info=raw.info,
        picks=raw_picks,
        n_components=5,
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_013.png
   :alt: Component Patterns, Comp 0, Comp 1, Comp 2, Comp 3, Comp 4
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_013.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x1200 with 8 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 339-341

Component Summary
Expectation: PSD peak in 8-12 Hz range.

.. GENERATED FROM PYTHON SOURCE LINES 341-350

.. code-block:: Python

    plot_component_summary(
        dss_alpha,
        data=raw,
        info=raw.info,
        picks=raw_picks,
        n_components=[0],
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_014.png
   :alt: Comp 0 Pattern, Comp 0 Time Course, PSD
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_014.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 351-352

Denoising Comparison

.. GENERATED FROM PYTHON SOURCE LINES 352-364

.. code-block:: Python

    print("Reconstructing Alpha component...")
    sources_alpha = dss_alpha.transform(raw)
    sources_alpha[1:, :] = 0
    raw_alpha = dss_alpha.inverse_transform(sources_alpha)
    raw_alpha = mne.io.RawArray(raw_alpha, raw.info)

    # Compare PSDs
    # Expectation: Denoised signal roughly follows original in
    # alpha band but has lower noise floor.
    plot_psd_comparison(raw, raw_alpha, fmax=40, show=True)





.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_015.png
   :alt: PSD Comparison
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_015.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Reconstructing Alpha component...
    Creating RawArray with float64 data, n_channels=203, n_times=36038
        Range : 0 ... 36037 =      0.000 ...    60.000 secs
    Ready.
    Effective window size : 3.410 (s)
    Effective window size : 3.410 (s)

    <Figure size 1600x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 365-369

Real B: Trial Average Bias (Auditory Evoked)
--------------------------------------------
Goal: Find the M100 auditory response.
We first need to epoch the data around auditory events.

.. GENERATED FROM PYTHON SOURCE LINES 369-393

.. code-block:: Python


    print("\n--- Real: Trial Average Bias (M100) ---")
    events = mne.read_events(event_fname)
    # Event ID 1 = Auditory/Left
    epochs_real = mne.Epochs(
        raw,
        events,
        event_id=1,
        tmin=-0.1,
        tmax=0.4,
        baseline=(None, 0),
        preload=True,
        verbose=False,
    )
    print(f"Epochs extracted: {len(epochs_real)}")
    epochs_real_picks = np.arange(len(epochs_real.ch_names))

    dss_m100 = DSS(n_components=5, bias=AverageBias())
    dss_m100.fit(epochs_real)

    # Visualize
    # Score Curve
    plot_component_score_curve(dss_m100, mode="ratio", show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_016.png
   :alt: Component Scores
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_016.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    --- Real: Trial Average Bias (M100) ---
    Epochs extracted: 20
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:496: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      baseline_cov = mne.compute_covariance(inst, method=method, **kws)
    /home/runner/work/mne-denoise/mne-denoise/mne_denoise/dss/linear.py:498: RuntimeWarning: Epochs are not baseline corrected, covariance matrix may be inaccurate
      biased_cov = mne.compute_covariance(biased_inst, method=method, **kws)

    <Figure size 1400x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 394-398

Component Time Series
Expectation: Comp 0 should show a clear evoked potential (M100) that is
visible even in the stacked single trials (if SNR is good
enough) or at least in the mean.

.. GENERATED FROM PYTHON SOURCE LINES 398-400

.. code-block:: Python

    plot_component_time_series(dss_m100, data=epochs_real, n_components=5, show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_017.png
   :alt: Component Time Series
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_017.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2000x800 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 401-404

Spatial Patterns
Expectation: Dipolar pattern over auditory cortex (temporal lobes).
Observation: You might see symmetric dipoles over left and right temporal areas.

.. GENERATED FROM PYTHON SOURCE LINES 404-412

.. code-block:: Python

    plot_component_patterns(
        dss_m100,
        info=epochs_real.info,
        picks=epochs_real_picks,
        n_components=5,
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_018.png
   :alt: Component Patterns, Comp 0, Comp 1, Comp 2, Comp 3, Comp 4
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_018.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x1200 with 8 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 413-414

Summary

.. GENERATED FROM PYTHON SOURCE LINES 414-423

.. code-block:: Python

    plot_component_summary(
        dss_m100,
        data=epochs_real,
        info=epochs_real.info,
        picks=epochs_real_picks,
        n_components=[0],
        show=True,
    )




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_019.png
   :alt: Comp 0 Pattern, Comp 0 Time Course, PSD
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_019.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <Figure size 2400x600 with 3 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 424-425

Denoising Comparison

.. GENERATED FROM PYTHON SOURCE LINES 425-435

.. code-block:: Python

    print("Reconstructing M100 component...")
    sources = dss_m100.transform(epochs_real)
    sources[:, 1:, :] = 0
    epochs_m100 = dss_m100.inverse_transform(sources)
    epochs_m100 = mne.EpochsArray(epochs_m100, epochs_real.info)

    # Compare Evoked Responses
    # Expectation: Cleaner M100 peak with reduced baseline noise.
    plot_evoked_gfp_comparison(epochs_real, epochs_m100, times=epochs_real.times, show=True)




.. image-sg:: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_020.png
   :alt: Evoked GFP Comparison
   :srcset: /auto_examples/dss/images/sphx_glr_plot_01_dss_fundamentals_020.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Reconstructing M100 component...
    Not setting metadata
    20 matching events found
    No baseline correction applied
    0 projection items activated

    <Figure size 2000x1200 with 1 Axes>



.. GENERATED FROM PYTHON SOURCE LINES 436-444

Conclusion
----------
We successfully demonstrated the flexibility of DSS:
AverageBias: Found phase-locked signals (Sine wave, M100) by averaging.
BandpassBias: Found induced/oscillatory signals (50Hz, Alpha) by filtering.

The same algorithm, `DSS`, solved both problems simply by
changing the definition of "interesting".


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 2.361 seconds)


.. _sphx_glr_download_auto_examples_dss_plot_01_dss_fundamentals.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_01_dss_fundamentals.ipynb <plot_01_dss_fundamentals.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_01_dss_fundamentals.py <plot_01_dss_fundamentals.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_01_dss_fundamentals.zip <plot_01_dss_fundamentals.zip>`