deploy: braindecode/braindecode@fe0511f

Author: bruAristimunha

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -53,7 +53,7 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
 ######################################################################
 # Preprocessing

dev/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
       ]
     },
     {

dev/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
       ]
     },
     {

dev/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb

@@ -43,7 +43,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
       ]
     },
     {

dev/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb

@@ -0,0 +1,215 @@
+""".. _preprocessing-classes:
+
+Comprehensive Preprocessing with MNE-based Classes
+===================================================
+
+This example demonstrates the various preprocessing classes available in
+Braindecode that wrap MNE-Python functionality. These classes provide a
+convenient and type-safe way to preprocess EEG data.
+"""
+
+# Authors: Bruno Aristimunha <b.aristimunha@gmail.com>
+#
+# License: BSD (3-clause)
+
+import mne
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import (
+    Anonymize,
+    ApplyHilbert,
+    Crop,
+    Filter,
+    Pick,
+    Resample,
+    SetEEGReference,
+    SetMontage,
+    preprocess,
+)
+
+###############################################################################
+# Load a sample dataset
+# ---------------------
+# We'll use a small MOABB dataset for demonstration
+
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+###############################################################################
+# Signal Processing
+# -----------------
+# Apply common signal processing operations
+
+# 1. Resample to reduce computational load
+print(f"Original sampling frequency: {dataset.datasets[0].raw.info['sfreq']} Hz")
+preprocessors_signal = [
+    Resample(sfreq=100),  # Downsample to 100 Hz
+]
+preprocess(dataset, preprocessors_signal)
+print(f"After resampling: {dataset.datasets[0].raw.info['sfreq']} Hz")
+
+# 2. Remove power line noise and apply bandpass filter
+preprocessors_filtering = [
+    Filter(l_freq=4, h_freq=30),  # Bandpass filter 4-30 Hz
+]
+preprocess(dataset, preprocessors_filtering)
+print("Applied bandpass filter 4-30 Hz")
+
+###############################################################################
+# Channel Management
+# ------------------
+# Select and manipulate channels
+
+# 3. Pick only EEG channels
+preprocessors_channels = [
+    Pick(picks="eeg"),  # Select only EEG channels
+]
+print(f"Channels before pick: {len(dataset.datasets[0].raw.ch_names)}")
+preprocess(dataset, preprocessors_channels)
+print(f"Channels after pick: {len(dataset.datasets[0].raw.ch_names)}")
+
+# 4. Rename channels (example - just for demonstration)
+original_names = dataset.datasets[0].raw.ch_names[:3]
+print(f"Original channel names (first 3): {original_names}")
+# Note: We won't actually rename to avoid breaking the example,
+# but this is how you would do it:
+# preprocessors_rename = [
+#     RenameChannels(mapping={'C3': 'C3_renamed', 'C4': 'C4_renamed'}),
+# ]
+# preprocess(dataset, preprocessors_rename)
+
+###############################################################################
+# Reference & Montage
+# -------------------
+# Set reference and channel positions
+
+# 5. Set EEG reference to average
+preprocessors_reference = [
+    SetEEGReference(ref_channels="average"),
+]
+preprocess(dataset, preprocessors_reference)
+print("Set EEG reference to average")
+
+# 6. Set montage for proper channel positions
+montage = mne.channels.make_standard_montage("standard_1020")
+preprocessors_montage = [
+    SetMontage(montage=montage, match_case=False, on_missing="ignore"),
+]
+preprocess(dataset, preprocessors_montage)
+print(
+    f"Set montage, number of positions: {len(dataset.datasets[0].raw.get_montage().get_positions()['ch_pos'])}"
+)
+
+###############################################################################
+# Data Transformation
+# -------------------
+# Apply transformations to the data
+
+# 7. Crop data to specific time range
+preprocessors_crop = [
+    Crop(tmin=0, tmax=60),  # Keep only first 60 seconds
+]
+print(f"Data duration before crop: {dataset.datasets[0].raw.times[-1]:.1f} s")
+preprocess(dataset, preprocessors_crop)
+print(f"Data duration after crop: {dataset.datasets[0].raw.times[-1]:.1f} s")
+
+###############################################################################
+# Metadata & Configuration
+# -------------------------
+# Modify metadata and configuration
+
+# 8. Anonymize measurement information
+preprocessors_anonymize = [
+    Anonymize(),
+]
+preprocess(dataset, preprocessors_anonymize)
+print("Anonymized measurement information")
+
+###############################################################################
+# Advanced: Envelope Extraction
+# ------------------------------
+# Extract signal envelope using Hilbert transform
+
+# 9. Compute envelope (useful for some analyses)
+# Note: This modifies the data, so use carefully
+preprocessors_envelope = [
+    ApplyHilbert(envelope=True),
+]
+preprocess(dataset, preprocessors_envelope)
+print("Computed signal envelope")
+
+###############################################################################
+# Combining Multiple Preprocessing Steps
+# ---------------------------------------
+# You can combine multiple preprocessing steps in a single pipeline
+
+print("\n" + "=" * 60)
+print("Complete Preprocessing Pipeline Example")
+print("=" * 60)
+
+# Reload dataset for complete pipeline demonstration
+dataset_complete = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+# Set montage first (needed for interpolation)
+montage = mne.channels.make_standard_montage("standard_1020")
+
+complete_pipeline = [
+    # 1. Set montage
+    SetMontage(montage=montage, match_case=False, on_missing="ignore"),
+    # 2. Set reference
+    SetEEGReference(ref_channels="average"),
+    # 3. Bandpass filter
+    Filter(l_freq=4, h_freq=30),
+    # 4. Downsample
+    Resample(sfreq=100),
+    # 5. Select only EEG channels
+    Pick(picks="eeg"),
+    # 6. Crop to region of interest
+    Crop(tmin=0, tmax=60),
+    # 7. Anonymize
+    Anonymize(),
+]
+
+print(
+    f"Original: {dataset_complete.datasets[0].raw.info['sfreq']} Hz, "
+    f"{dataset_complete.datasets[0].raw.times[-1]:.1f} s, "
+    f"{len(dataset_complete.datasets[0].raw.ch_names)} channels"
+)
+
+preprocess(dataset_complete, complete_pipeline)
+
+print(
+    f"After preprocessing: {dataset_complete.datasets[0].raw.info['sfreq']} Hz, "
+    f"{dataset_complete.datasets[0].raw.times[-1]:.1f} s, "
+    f"{len(dataset_complete.datasets[0].raw.ch_names)} channels"
+)
+
+print("\nPreprocessing complete!")
+
+###############################################################################
+# Summary
+# -------
+# Braindecode provides 45 preprocessing classes that wrap MNE-Python
+# functionality:
+#
+# **Signal Processing**: Resample, Filter, NotchFilter, SavgolFilter,
+# ApplyHilbert, Rescale, OversampledTemporalProjection
+#
+# **Channel Management**: Pick, PickChannels, PickTypes, DropChannels,
+# AddChannels, CombineChannels, RenameChannels, ReorderChannels,
+# SetChannelTypes, InterpolateBads, InterpolateTo, InterpolateBridgedElectrodes,
+# ComputeBridgedElectrodes, EqualizeChannels
+#
+# **Reference & Montage**: SetEEGReference, AddReferenceChannels, SetMontage
+#
+# **SSP Projections**: AddProj, ApplyProj, DelProj
+#
+# **Data Transformation**: Crop, CropByAnnotations, ComputeCurrentSourceDensity,
+# FixStimArtifact, MaxwellFilter, RealignRaw, RegressArtifact
+#
+# **Artifact Detection & Annotation**: AnnotateAmplitude, AnnotateBreak,
+# AnnotateMovement, AnnotateMuscleZscore, AnnotateNan
+#
+# **Metadata & Configuration**: Anonymize, SetAnnotations, SetMeasDate,
+# AddEvents, FixMagCoilTypes, ApplyGradientCompensation
+#
+# See the API documentation for details on each class and their parameters.