Upload BNCI2014_001 EEGWindowsDataset (continuous)

.gitattributes

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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
+
+| Property | Value |
+|---|---:|
+| Number of recordings | 1 |
+| Dataset type | Windowed (from Raw object) |
+| Number of channels | 26 |
+| Sampling frequency | 250 Hz |
+| Number of windows / samples | 48 |
+| Total size | 19.22 MB |
+| Storage format | zarr |
+
+## Usage
+
+To load this dataset::
+
+    .. code-block:: 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
+
+::
+
+    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, metainfo in train_loader:
+        # X shape: [batch_size, n_channels, n_times]
+        # y shape: [batch_size]
+        # metainfo shape: [batch_size, 2] (start and end indices)
+        # 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

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dataset.zarr/recording_0/data/zarr.json

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+}

dataset.zarr/recording_0/zarr.json

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+{
+  "attributes": {
+    "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]]}",
+    "description": "{\"subject\":1,\"session\":\"0train\",\"run\":\"0\"}",
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+  },
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+  "consolidated_metadata": null,
+  "node_type": "group"
+}

dataset.zarr/zarr.json

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+{
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+    "n_datasets": 1,
+    "dataset_type": "EEGWindowsDataset",
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+  },
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+  "consolidated_metadata": null,
+  "node_type": "group"
+}

format_info.json

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+  "format": "zarr",
+  "compression": "blosc",
+  "compression_level": 5,
+  "braindecode_version": "1.3.0",
+  "n_recordings": 1,
+  "total_samples": 48,
+  "total_size_mb": 19.22
+}