Test EEGWindowsDataset with refactored code

.gitattributes

@@ -57,3 +57,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+tags:
+- braindecode
+- eeg
+- neuroscience
+- brain-computer-interface
+license: unknown
+---
+
+# EEG Dataset
+
+This dataset was created using [braindecode](https://braindecode.org), a library for deep learning with EEG/MEG/ECoG signals.
+
+## Dataset Information
+
+- **Number of recordings**: 1
+- **Number of channels**: 26
+- **Sampling frequency**: 250.0 Hz
+- **Data type**: Windowed (from Raw object)
+- **Number of windows**: 48
+- **Total size**: 19.23 MB
+- **Storage format**: zarr
+
+## Usage
+
+To load this dataset:
+
+```python
+from braindecode.datasets import BaseConcatDataset
+
+# Load dataset from Hugging Face Hub
+dataset = BaseConcatDataset.from_pretrained("username/dataset-name")
+
+# Access data
+X, y, metainfo = dataset[0]
+# X: EEG data (n_channels, n_times)
+# y: label/target
+# metainfo: window indices
+```
+
+## Using with PyTorch DataLoader
+
+```python
+from torch.utils.data import DataLoader
+
+# Create DataLoader for training
+train_loader = DataLoader(
+    dataset,
+    batch_size=32,
+    shuffle=True,
+    num_workers=4
+)
+
+# Training loop
+for X, y, _ in train_loader:
+    # X shape: [batch_size, n_channels, n_times]
+    # y shape: [batch_size]
+    # Process your batch...
+```
+
+## Dataset Format
+
+This dataset is stored in **Zarr** format, optimized for:
+- Fast random access during training (critical for PyTorch DataLoader)
+- Efficient compression with blosc
+- Cloud-native storage compatibility
+
+For more information about braindecode, visit: https://braindecode.org

dataset.zarr/.zattrs

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+{
+    "braindecode_version": "1.0",
+    "dataset_type": "EEGWindowsDataset",
+    "n_datasets": 1,
+    "window_kwargs": "[[\"create_windows_from_events\", {\"infer_mapping\": true, \"infer_window_size_stride\": true, \"trial_start_offset_samples\": 0, \"trial_stop_offset_samples\": 0, \"window_stride_samples\": null, \"drop_last_window\": false, \"preload\": false, \"drop_bad_windows\": null, \"picks\": null, \"reject\": null, \"flat\": null, \"on_missing\": \"error\", \"accepted_bads_ratio\": 0.0, \"verbose\": \"error\", \"use_mne_epochs\": false, \"mapping\": {\"feet\": 0, \"left_hand\": 1, \"right_hand\": 2, \"tongue\": 3}, \"window_size_samples\": null}]]"
+}

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+    "description": "{\"subject\":1,\"session\":\"0train\",\"run\":\"0\"}",
+    "info": "{\"ch_names\": [\"Fz\", \"FC3\", \"FC1\", \"FCz\", \"FC2\", \"FC4\", \"C5\", \"C3\", \"C1\", \"Cz\", \"C2\", \"C4\", \"C6\", \"CP3\", \"CP1\", \"CPz\", \"CP2\", \"CP4\", \"P1\", \"Pz\", \"P2\", \"POz\", \"EOG1\", \"EOG2\", \"EOG3\", \"stim\"], \"sfreq\": 250.0, \"ch_types\": [\"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eog\", \"eog\", \"eog\", \"stim\"], \"lowpass\": 125.0, \"highpass\": 0.0}",
+    "last_target_only": true,
+    "metadata": "{\"columns\":[\"i_window_in_trial\",\"i_start_in_trial\",\"i_stop_in_trial\",\"target\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],\"data\":[[0,750,1750,3],[0,2753,3753,0],[0,4671,5671,2],[0,6623,7623,1],[0,8631,9631,1],[0,10742,11742,2],[0,12659,13659,0],[0,14709,15709,3],[0,16640,17640,2],[0,18638,19638,0],[0,20544,21544,1],[0,22439,23439,1],[0,24411,25411,1],[0,26355,27355,3],[0,28322,29322,2],[0,30442,31442,2],[0,32450,33450,1],[0,34516,35516,1],[0,36552,37552,0],[0,38618,39618,1],[0,40688,41688,2],[0,42768,43768,3],[0,44680,45680,3],[0,46711,47711,0],[0,48664,49664,1],[0,50625,51625,3],[0,52729,53729,3],[0,54733,55733,2],[0,56708,57708,3],[0,58734,59734,3],[0,60805,61805,2],[0,62912,63912,1],[0,65004,66004,2],[0,67095,68095,0],[0,69138,70138,0],[0,71202,72202,0],[0,73222,74222,3],[0,75194,76194,0],[0,77157,78157,1],[0,79082,80082,3],[0,81163,82163,2],[0,83141,84141,0],[0,85131,86131,2],[0,87250,88250,0],[0,89156,90156,3],[0,91084,92084,2],[0,93198,94198,0],[0,95257,96257,1]]}",
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+}

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format_info.json

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