Upload BNCI2014_001 RawDataset

.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 | Continuous (Raw) |
+| Number of channels | 26 |
+| Sampling frequency | 250 Hz |
+| Number of windows / samples | 96735 |
+| 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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+    96735
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+  "chunk_grid": {
+    "name": "regular",
+    "configuration": {
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+      "configuration": {
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+  "storage_transformers": []
+}

dataset.zarr/recording_0/zarr.json

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+{
+  "attributes": {
+    "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}"
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+  "consolidated_metadata": null,
+  "node_type": "group"
+}

dataset.zarr/zarr.json

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+{
+  "attributes": {
+    "n_datasets": 1,
+    "dataset_type": "RawDataset",
+    "braindecode_version": "1.3.0"
+  },
+  "zarr_format": 3,
+  "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": 96735,
+  "total_size_mb": 19.22
+}