Delete README.md with huggingface_hub

README.md

@@ -1,102 +0,0 @@
----
-tags:
-- braindecode
-- eeg
-- neuroscience
-- brain-computer-interface
-- deep-learning
-license: unknown
----
-
-# EEG Dataset
-
-This dataset was created using [braindecode](https://braindecode.org), a deep
-learning library for EEG/MEG/ECoG signals.
-
-## Dataset Information
-
-| Property | Value |
-|----------|------:|
-| Recordings | 1 |
-| Type | Windowed (from Raw object) |
-| Channels | 26 |
-| Sampling frequency | 250 Hz |
-| Total duration | 0:06:26 |
-| Windows/samples | 48 |
-| Size | 19.22 MB |
-| Format | zarr |
-
-## Quick Start
-
-```python
-from braindecode.datasets import BaseConcatDataset
-
-# Load from Hugging Face Hub
-dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")
-
-# Access a sample
-X, y, metainfo = dataset[0]
-# X: EEG data [n_channels, n_times]
-# y: target label
-# metainfo: window indices
-```
-
-## Training with PyTorch
-
-```python
-from torch.utils.data import DataLoader
-
-loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
-
-for X, y, metainfo in loader:
-    # X: [batch_size, n_channels, n_times]
-    # y: [batch_size]
-    pass  # Your training code
-```
-
-## BIDS-inspired Structure
-
-This dataset uses a **BIDS-inspired** organization. Metadata files follow BIDS
-conventions, while data is stored in Zarr format for efficient deep learning.
-
-**BIDS-style metadata:**
-- `dataset_description.json` - Dataset information
-- `participants.tsv` - Subject metadata
-- `*_events.tsv` - Trial/window events
-- `*_channels.tsv` - Channel information
-- `*_eeg.json` - Recording parameters
-
-**Data storage:**
-- `dataset.zarr/` - Zarr format (optimized for random access)
-
-```
-sourcedata/braindecode/
-├── dataset_description.json
-├── participants.tsv
-├── dataset.zarr/
-└── sub-<label>/
-    └── eeg/
-        ├── *_events.tsv
-        ├── *_channels.tsv
-        └── *_eeg.json
-```
-
-### Accessing Metadata
-
-```python
-# Participants info
-if hasattr(dataset, "participants"):
-    print(dataset.participants)
-
-# Events for a recording
-if hasattr(dataset.datasets[0], "bids_events"):
-    print(dataset.datasets[0].bids_events)
-
-# Channel info
-if hasattr(dataset.datasets[0], "bids_channels"):
-    print(dataset.datasets[0].bids_channels)
-```
-
----
-
-*Created with [braindecode](https://braindecode.org)*