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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_auto_examples_core_tutorial_eoec.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_generated_auto_examples_core_tutorial_eoec.py:

.. _tutorial-eoec:

Eyes Open vs. Closed Classification
===================================

EEGDash example for eyes open vs. closed classification.

This example uses the :mod:`eegdash` library in combination with PyTorch to develop a deep learning model for analyzing EEG data, specifically for eyes open vs. closed classification in a single subject.

1. **Data Retrieval Using EEGDash**: An instance of :class:`eegdash.api.EEGDashDataset` is created to search and retrieve an EEG dataset. At this step, only the metadata is transferred.

2. **Data Preprocessing Using BrainDecode**: This process preprocesses EEG data using Braindecode by reannotating events, selecting specific channels, resampling, filtering, and extracting 2-second epochs, ensuring balanced eyes-open and eyes-closed data for analysis.

3. **Creating train and testing sets**: The dataset is split into training (80%) and testing (20%) sets with balanced labels, converted into PyTorch tensors, and wrapped in DataLoader objects for efficient mini-batch training.

4. **Model Definition**: The model is a shallow convolutional neural network (ShallowFBCSPNet) with 24 input channels (EEG channels), 2 output classes (eyes-open and eyes-closed).

5. **Model Training and Evaluation Process**: This section trains the neural network, normalizes input data, computes cross-entropy loss, updates model parameters, and evaluates classification accuracy over six epochs.

.. GENERATED FROM PYTHON SOURCE LINES 22-30

Data Retrieval Using EEGDash
----------------------------

This section instantiates :class:`eegdash.api.EEGDashDataset` to fetch
the metadata for the experiment before requesting any recordings.

First we find one resting state dataset. This dataset contains both eyes open
and eyes closed data.

.. GENERATED FROM PYTHON SOURCE LINES 30-33

.. code-block:: Python

    from pathlib import Path

    cache_folder = Path.home() / "eegdash"







.. GENERATED FROM PYTHON SOURCE LINES 34-41

.. code-block:: Python

    from eegdash import EEGDashDataset

    ds_eoec = EEGDashDataset(
        query={"dataset": "ds005514", "task": "RestingState", "subject": "NDARDB033FW5"},
        cache_dir=cache_folder,
    )





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

 .. code-block:: none

    [10/13/25 00:28:51] WARNING  Cache directory does not exist, creating api.py:726
                                 it: /home/runner/eegdash                           
    ╭────────────────────── EEG 2025 Competition Data Notice ──────────────────────╮
    │ This notice is only for users who are participating in the EEG 2025          │
    │ Competition.                                                                 │
    │                                                                              │
    │ EEG 2025 Competition Data Notice!                                            │
    │ You are loading one of the datasets that is used in competition, but via     │
    │ `EEGDashDataset`.                                                            │
    │                                                                              │
    │ IMPORTANT:                                                                   │
    │ If you download data from `EEGDashDataset`, it is NOT identical to the       │
    │ official                                                                     │
    │ competition data, which is accessed via `EEGChallengeDataset`. The           │
    │ competition data has been downsampled and filtered.                          │
    │                                                                              │
    │ If you are participating in the competition,                                 │
    │ you must use the `EEGChallengeDataset` object to ensure consistency.         │
    │                                                                              │
    │ If you are not participating in the competition, you can ignore this         │
    │ message.                                                                     │
    ╰─────────────────────────── Source: EEGDashDataset ───────────────────────────╯




.. GENERATED FROM PYTHON SOURCE LINES 42-72

Data Preprocessing Using Braindecode
------------------------------------

`braindecode <https://braindecode.org/stable/install/install.html>`__ is a
specialized library for preprocessing EEG and MEG data. In this dataset, there
are two key events in the continuous data: **instructed_toCloseEyes**, marking
the start of a 40-second eyes-closed period, and **instructed_toOpenEyes**,
indicating the start of a 20-second eyes-open period.

For the eyes-closed event, we extract 14 seconds of data from 15 to 29 seconds
after the event onset. Similarly, for the eyes-open event, we extract data
from 5 to 19 seconds after the event onset. This ensures an equal amount of
data for both conditions. The event extraction is handled by the custom
function :func:`eegdash.hbn.preprocessing.hbn_ec_ec_reannotation`.

Next, we apply four preprocessing steps in Braindecode:
1. **Reannotation** of event markers using :func:`eegdash.hbn.preprocessing.hbn_ec_ec_reannotation`.
2. **Selection** of 24 specific EEG channels from the original 128.
3. **Resampling** the EEG data to a frequency of 128 Hz.
4. **Filtering** the EEG signals to retain frequencies between 1 Hz and 55 Hz.

When calling the `preprocess` function, the data is retrieved from the remote
repository.

Finally, we use `create_windows_from_events` to extract 2-second epochs from
the data. These epochs serve as the dataset samples. At this stage, each
sample is automatically labeled with the corresponding event type (eyes-open
or eyes-closed). `windows_ds` is a PyTorch dataset, and when queried, it
returns labels for eyes-open and eyes-closed (assigned as labels 0 and 1,
corresponding to their respective event markers).

.. GENERATED FROM PYTHON SOURCE LINES 74-131

.. code-block:: Python

    from braindecode.preprocessing import (
        preprocess,
        Preprocessor,
        create_windows_from_events,
    )
    import numpy as np
    from eegdash.hbn.preprocessing import hbn_ec_ec_reannotation
    import warnings

    warnings.simplefilter("ignore", category=RuntimeWarning)


    # BrainDecode preprocessors
    preprocessors = [
        hbn_ec_ec_reannotation(),
        Preprocessor(
            "pick_channels",
            ch_names=[
                "E22",
                "E9",
                "E33",
                "E24",
                "E11",
                "E124",
                "E122",
                "E29",
                "E6",
                "E111",
                "E45",
                "E36",
                "E104",
                "E108",
                "E42",
                "E55",
                "E93",
                "E58",
                "E52",
                "E62",
                "E92",
                "E96",
                "E70",
                "Cz",
            ],
        ),
        Preprocessor("resample", sfreq=128),
        Preprocessor("filter", l_freq=1, h_freq=55),
    ]
    preprocess(ds_eoec, preprocessors)

    # Extract 2-second segments
    windows_ds = create_windows_from_events(
        ds_eoec,
        trial_start_offset_samples=0,
        trial_stop_offset_samples=256,
        preload=True,
    )





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    Used Annotations descriptions: [np.str_('boundary'), np.str_('break cnt'), np.str_('instructed_toCloseEyes'), np.str_('instructed_toOpenEyes'), np.str_('resting_start')]
    [10/13/25 00:29:12] INFO     Original events found with ids: preprocessing.py:66
                                 {np.str_('boundary'): 1,                           
                                 np.str_('break cnt'): 2,                           
                                 np.str_('instructed_toCloseEyes                    
                                 '): 3,                                             
                                 np.str_('instructed_toOpenEyes'                    
                                 ): 4, np.str_('resting_start'):                    
                                 5}                                                 
    NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).
    Filtering raw data in 1 contiguous segment
    Setting up band-pass filter from 1 - 55 Hz

    FIR filter parameters
    ---------------------
    Designing a one-pass, zero-phase, non-causal bandpass filter:
    - Windowed time-domain design (firwin) method
    - Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
    - Lower passband edge: 1.00
    - Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
    - Upper passband edge: 55.00 Hz
    - Upper transition bandwidth: 9.00 Hz (-6 dB cutoff frequency: 59.50 Hz)
    - Filter length: 423 samples (3.305 s)

    Used Annotations descriptions: [np.str_('eyes_closed'), np.str_('eyes_open')]




.. GENERATED FROM PYTHON SOURCE LINES 132-138

Plotting a Single Channel for One Sample
----------------------------------------

It’s always a good practice to verify that the data has been properly loaded
and processed. Here, we plot a single channel from one sample to ensure the
signal is present and looks as expected.

.. GENERATED FROM PYTHON SOURCE LINES 140-146

.. code-block:: Python

    import matplotlib.pyplot as plt

    plt.figure()
    plt.plot(windows_ds[2][0][0, :].transpose())  # first channel of first epoch
    plt.show()




.. image-sg:: /generated/auto_examples/core/images/sphx_glr_tutorial_eoec_001.png
   :alt: tutorial eoec
   :srcset: /generated/auto_examples/core/images/sphx_glr_tutorial_eoec_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 147-171

Creating training and test sets
-------------------------------

The code below creates a training and test set. We first split the data into
training and test sets using the **train_test_split** function from the
**sklearn** library. We then create a **TensorDataset** for the training and
test sets.

1. **Set Random Seed** – The random seed is fixed using
   `torch.manual_seed(random_state)` to ensure reproducibility in dataset
   splitting and model training.
2. **Extract Labels from the Dataset** – Labels (eye-open or eye-closed
   events) are extracted from `windows_ds`, stored as a NumPy array, and
   printed for verification.
3. **Split Dataset into Train and Test Sets** – The dataset is split into
   training (80%) and testing (20%) subsets using `train_test_split()`,
   ensuring balanced stratification based on the extracted labels.
4. **Convert Data to PyTorch Tensors** – The selected training and testing
   samples are converted into `FloatTensor` for input features and
   `LongTensor` for labels, making them compatible with PyTorch models.
5. **Create DataLoaders** – The datasets are wrapped in PyTorch DataLoader
   objects with a batch size of 10, enabling efficient mini-batch training and
   shuffling.


.. GENERATED FROM PYTHON SOURCE LINES 173-216

.. code-block:: Python

    import torch
    from sklearn.model_selection import train_test_split
    from torch.utils.data import DataLoader
    from torch.utils.data import TensorDataset

    # Set random seed for reproducibility
    random_state = 42
    torch.manual_seed(random_state)
    np.random.seed(random_state)

    # Extract labels from the dataset
    eo_ec = np.array([ds[1] for ds in windows_ds]).transpose()  # check labels
    print("labels: ", eo_ec)

    # Get balanced indices for male and female subjects
    train_indices, test_indices = train_test_split(
        range(len(windows_ds)), test_size=0.2, stratify=eo_ec, random_state=random_state
    )

    # Convert the data to tensors
    X_train = torch.FloatTensor(
        np.array([windows_ds[i][0] for i in train_indices])
    )  # Convert list of arrays to single tensor
    X_test = torch.FloatTensor(
        np.array([windows_ds[i][0] for i in test_indices])
    )  # Convert list of arrays to single tensor
    y_train = torch.LongTensor(eo_ec[train_indices])  # Convert targets to tensor
    y_test = torch.LongTensor(eo_ec[test_indices])  # Convert targets to tensor
    dataset_train = TensorDataset(X_train, y_train)
    dataset_test = TensorDataset(X_test, y_test)

    # Create data loaders for training and testing (batch size 10)
    train_loader = DataLoader(dataset_train, batch_size=10, shuffle=True)
    test_loader = DataLoader(dataset_test, batch_size=10, shuffle=True)

    # Print shapes and sizes to verify split
    print(
        f"Shape of data {X_train.shape} number of samples - Train: {len(train_loader)}, Test: {len(test_loader)}"
    )
    print(
        f"Eyes-Open/Eyes-Closed balance, train: {np.mean(eo_ec[train_indices]):.2f}, test: {np.mean(eo_ec[test_indices]):.2f}"
    )





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

 .. code-block:: none

    labels:  [1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0
     0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0]
    Shape of data torch.Size([56, 24, 256]) number of samples - Train: 6, Test: 2
    Eyes-Open/Eyes-Closed balance, train: 0.50, test: 0.50




.. GENERATED FROM PYTHON SOURCE LINES 217-224

Check labels
------------

It is good practice to verify the labels and ensure the random seed is
functioning correctly. If all labels are 0s (eyes closed) or 1s (eyes open),
it could indicate an issue with data loading or stratification, requiring
further investigation.

.. GENERATED FROM PYTHON SOURCE LINES 226-227

Visualize a batch of target labels

.. GENERATED FROM PYTHON SOURCE LINES 227-231

.. code-block:: Python

    dataiter = iter(train_loader)
    first_item, label = dataiter.__next__()
    label





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

 .. code-block:: none


    tensor([0, 1, 1, 1, 1, 0, 1, 1, 0, 0])



.. GENERATED FROM PYTHON SOURCE LINES 232-238

Create model
------------

The model is a shallow convolutional neural network (ShallowFBCSPNet) with 24
input channels (EEG channels), 2 output classes (eyes-open and eyes-closed),
and an input window size of 256 samples (2 seconds of EEG data).

.. GENERATED FROM PYTHON SOURCE LINES 240-250

.. code-block:: Python

    import torch
    import numpy as np
    from torch.nn import functional as F
    from braindecode.models import ShallowFBCSPNet
    from torchinfo import summary

    torch.manual_seed(random_state)
    model = ShallowFBCSPNet(24, 2, n_times=256, final_conv_length="auto")
    summary(model, input_size=(1, 24, 256))





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

 .. code-block:: none

    ==========================================================================================
    Layer (type:depth-idx)                   Output Shape              Param #
    ==========================================================================================
    ShallowFBCSPNet                          [1, 2]                    --
    ├─Ensure4d: 1-1                          [1, 24, 256, 1]           --
    ├─Rearrange: 1-2                         [1, 1, 256, 24]           --
    ├─CombinedConv: 1-3                      [1, 40, 232, 1]           39,440
    ├─BatchNorm2d: 1-4                       [1, 40, 232, 1]           80
    ├─Expression: 1-5                        [1, 40, 232, 1]           --
    ├─AvgPool2d: 1-6                         [1, 40, 11, 1]            --
    ├─SafeLog: 1-7                           [1, 40, 11, 1]            --
    ├─Dropout: 1-8                           [1, 40, 11, 1]            --
    ├─Sequential: 1-9                        [1, 2]                    --
    │    └─Conv2d: 2-1                       [1, 2, 1, 1]              882
    │    └─SqueezeFinalOutput: 2-2           [1, 2]                    --
    │    │    └─Rearrange: 3-1               [1, 2, 1]                 --
    ==========================================================================================
    Total params: 40,402
    Trainable params: 40,402
    Non-trainable params: 0
    Total mult-adds (Units.MEGABYTES): 0.00
    ==========================================================================================
    Input size (MB): 0.02
    Forward/backward pass size (MB): 0.07
    Params size (MB): 0.00
    Estimated Total Size (MB): 0.10
    ==========================================================================================

    ==========================================================================================
    Layer (type:depth-idx)                   Output Shape              Param #
    ==========================================================================================
    ShallowFBCSPNet                          [1, 2]                    --
    ├─Ensure4d: 1-1                          [1, 24, 256, 1]           --
    ├─Rearrange: 1-2                         [1, 1, 256, 24]           --
    ├─CombinedConv: 1-3                      [1, 40, 232, 1]           39,440
    ├─BatchNorm2d: 1-4                       [1, 40, 232, 1]           80
    ├─Expression: 1-5                        [1, 40, 232, 1]           --
    ├─AvgPool2d: 1-6                         [1, 40, 11, 1]            --
    ├─SafeLog: 1-7                           [1, 40, 11, 1]            --
    ├─Dropout: 1-8                           [1, 40, 11, 1]            --
    ├─Sequential: 1-9                        [1, 2]                    --
    │    └─Conv2d: 2-1                       [1, 2, 1, 1]              882
    │    └─SqueezeFinalOutput: 2-2           [1, 2]                    --
    │    │    └─Rearrange: 3-1               [1, 2, 1]                 --
    ==========================================================================================
    Total params: 40,402
    Trainable params: 40,402
    Non-trainable params: 0
    Total mult-adds (Units.MEGABYTES): 0.00
    ==========================================================================================
    Input size (MB): 0.02
    Forward/backward pass size (MB): 0.07
    Params size (MB): 0.00
    Estimated Total Size (MB): 0.10
    ==========================================================================================



.. GENERATED FROM PYTHON SOURCE LINES 251-275

Model Training and Evaluation Process
-------------------------------------

This section trains the neural network using the Adamax optimizer, normalizes
input data, computes cross-entropy loss, updates model parameters, and tracks
accuracy across six epochs.

1. **Set Up Optimizer and Learning Rate Scheduler** – The `Adamax` optimizer
   initializes with a learning rate of 0.002 and weight decay of 0.001 for
   regularization. An `ExponentialLR` scheduler with a decay factor of 1 keeps
   the learning rate constant.
2. **Allocate Model to Device** – The model moves to the specified device
   (CPU, GPU, or MPS for Mac silicon) to optimize computation efficiency.
3. **Normalize Input Data** – The `normalize_data` function standardizes input
   data by subtracting the mean and dividing by the standard deviation along
   the time dimension before transferring it to the appropriate device.
4. **Evaluates Classification Accuracy Over Six Epochs** – The training loop
   iterates through data batches with the model in training mode. It
   normalizes inputs, computes predictions, calculates cross-entropy loss,
   performs backpropagation, updates model parameters, and steps the learning
   rate scheduler. It tracks correct predictions to compute accuracy.
5. **Evaluate on Test Data** – After each epoch, the model runs in evaluation
   mode on the test set. It computes predictions on normalized data and
   calculates test accuracy by comparing outputs with actual labels.

.. GENERATED FROM PYTHON SOURCE LINES 277-328

.. code-block:: Python

    optimizer = torch.optim.Adamax(model.parameters(), lr=0.002, weight_decay=0.001)
    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=1)

    device = torch.device(
        "cuda"
        if torch.cuda.is_available()
        else "mps"
        if torch.backends.mps.is_available()
        else "cpu"
    )
    model = model.to(device=device)  # move the model parameters to CPU/GPU
    epochs = 6


    def normalize_data(x):
        mean = x.mean(dim=2, keepdim=True)
        std = x.std(dim=2, keepdim=True) + 1e-7  # add small epsilon for numerical stability
        x = (x - mean) / std
        x = x.to(device=device, dtype=torch.float32)  # move to device, e.g. GPU
        return x


    for e in range(epochs):
        # training
        correct_train = 0
        for t, (x, y) in enumerate(train_loader):
            model.train()  # put model to training mode
            scores = model(normalize_data(x))
            y = y.to(device=device, dtype=torch.long)
            _, preds = scores.max(1)
            correct_train += (preds == y).sum() / len(dataset_train)

            loss = F.cross_entropy(scores, y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            scheduler.step()

        # Validation
        correct_test = 0
        for t, (x, y) in enumerate(test_loader):
            model.eval()  # put model to testing mode
            scores = model(normalize_data(x))
            y = y.to(device=device, dtype=torch.long)
            _, preds = scores.max(1)
            correct_test += (preds == y).sum() / len(dataset_test)

        # Reporting
        print(
            f"Epoch {e}, Train accuracy: {correct_train:.2f}, Test accuracy: {correct_test:.2f}"
        )




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

 .. code-block:: none

    Epoch 0, Train accuracy: 0.66, Test accuracy: 0.50
    Epoch 1, Train accuracy: 0.79, Test accuracy: 0.50
    Epoch 2, Train accuracy: 0.91, Test accuracy: 0.50
    Epoch 3, Train accuracy: 0.88, Test accuracy: 0.57
    Epoch 4, Train accuracy: 0.91, Test accuracy: 0.57
    Epoch 5, Train accuracy: 0.88, Test accuracy: 0.50





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

   **Total running time of the script:** (0 minutes 23.357 seconds)

**Estimated memory usage:**  1316 MB


.. _sphx_glr_download_generated_auto_examples_core_tutorial_eoec.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/sccn/EEGDash/main?urlpath=lab/tree/notebooks/generated/auto_examples/core/tutorial_eoec.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_