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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
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+
+# Installer logs
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+# Unit test / coverage reports
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+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
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+
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+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+.pixi
+
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+
+# Celery stuff
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+
+# Redis
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+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
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+.abstra/
+
+# Visual Studio Code
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+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
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+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
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+
+# Streamlit
+.streamlit/secrets.toml

.python-version

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+3.12

LICENSE

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+MIT License
+
+Copyright (c) 2025 Jonathan Grizou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

README.md

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+---
+license: mit
+task_categories:
+  - other
+language:
+  - en
+tags:
+  - neuroscience
+  - brain-computer-interfacing
+  - eeg
+  - electroencephalography
+  - gan
+  - self-calibrating
+  - mental-imagery
+pretty_name: Self-Calibrating BCI Dataset (NeurIPS 2025)
+size_categories:
+  - 1K<n<10K
+---
+
+# Self-Calibrating BCI Dataset (NeurIPS 2025)
+
+**Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels**
+
+This dataset contains brain-computer interface (BCI) data from self-calibrating experiments where participants imagined target faces while EEG signals were recorded. The dataset includes:
+
+- Face representations in GAN latent space
+- Processed EEG features from neural networks
+- 9,234 experimental trials
+
+## 📊 Dataset Summary
+
+- **Domain**: Neuroscience, Brain-Computer Interfaces
+- **Task**: Mental imagery, face recognition, BCI calibration
+- **Size**: 9,234 samples, ~39 MB (HDF5 format)
+- **Format**: HDF5 (archival-grade, language-agnostic)
+- **License**: MIT
+
+## 🎯 Dataset Structure
+
+The data is stored in HDF5 format (`all_data_sorted.h5`) with three main arrays:
+
+| Array            | Shape       | Dtype   | Description                                  |
+| ---------------- | ----------- | ------- | -------------------------------------------- |
+| `target_faces`   | (9234, 512) | float64 | Target face latent vectors (Progressive GAN) |
+| `observed_faces` | (9234, 512) | float64 | Observed face latent vectors                 |
+| `eeg_net`        | (9234, 176) | float32 | Neural network processed EEG features        |
+
+All arrays are aligned: row `i` in each array corresponds to the same experimental trial.
+
+### Data Fields
+
+#### `target_faces`
+
+- **Description**: 512-dimensional latent vectors from Progressive GAN representing faces participants were trying to imagine
+- **GAN Model**: Progressive GAN trained on CelebA-HQ 1024×1024
+- **Value Range**: Approximately [-5, 5] (latent space coordinates)
+- **Usage**: Ground truth for BCI task; can be decoded to face images using GAN decoder
+
+#### `observed_faces`
+
+- **Description**: 512-dimensional latent vectors for faces actually presented/selected during trials
+- **Relationship**: Distance to `target_faces` measures BCI performance
+- **Usage**: Compare with `target_faces` to evaluate mental imagery accuracy
+
+#### `eeg_net`
+
+- **Description**: 176-dimensional learned representations from EEG signals
+- **Processing**: Neural network feature extraction from raw EEG data
+- **Electrodes**: Derived from 29-channel EEG system
+- **Usage**: Input features for BCI decoding models
+
+## 💻 Usage Examples
+
+Run `example_load_data.py`.
+
+## 📖 Data Collection
+
+**Experimental Setup:**
+
+- Participants imagined target faces while EEG was recorded
+- 29-channel EEG system
+- Face stimuli generated from Progressive GAN latent space
+- Self-calibrating paradigm (no labeled training data)
+
+**Processing Pipeline:**
+
+1. Raw EEG → Windowing & feature extraction → 203 features
+2. 203 features → Neural network → 176-dim embeddings (`eeg_net`)
+3. Face images → GAN encoder → 512-dim latent vectors
+
+## 📄 Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{grizou2025self,
+  title={Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels},
+  author={Grizou, Jonathan and de la Torre-Ortiz, Carlos and Ruotsalo, Tuukka},
+  journal={Advances in Neural Information Processing Systems},
+  year={2025}
+}
+```
+
+## 📜 License
+
+This dataset is released under the MIT License.
+
+## 🔗 Related Resources
+
+- **Paper Repository**: [github.com/jgrizou/neurips-self-calibrating-bci](https://github.com/jgrizou/neurips-self-calibrating-bci)
+- **GAN Checkpoint**: Progressive GAN for CelebA-HQ 1024×1024
+- **Contact**: jonathan.grizou@grizai.com
+
+## 🤝 Contributions
+
+This dataset was created as part of the NeurIPS 2025 paper. For questions, issues, or suggestions, please contact jonathan.grizou@grizai.com or open an issue on the paper repository.
+
+## 🙏 Acknowledgments
+
+Jonathan Grizou conducted this work during his tenure as an Assistant Professor at the University of Glasgow and subsequently through GrizAI Ltd. We gratefully acknowledge the financial support of both organizations.
+
+This research was partially funded by the Alfred Kordelin Foundation (grant 230099) and the Finnish Foundation for Technology Promotion (grant 10168).
+
+Computing and storage resources were provided by the Finnish Computing Competence Infrastructure (FCCI; HILE ERC grant ILLUMINATOR, 101114623).

data/eeg-net.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82cddd25951e742a1bc8596ccc53245b8b3530c68d870b3308d512ebc13618b0
+size 40673742

example_load_data.py

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+#!/usr/bin/env python3
+"""
+Example script demonstrating how to load and explore the Self-Calibrating BCI Dataset (NeurIPS 2025).
+
+This script shows:
+1. How to open and read the HDF5 file
+2. How to access the data arrays
+3. How to read embedded metadata
+4. Basic data exploration and statistics
+
+Requirements:
+    # Using uv (recommended)
+    uv sync
+    uv run python example_load_data.py
+
+    # Or using pip
+    pip install ... (check pyproject.toml)
+"""
+
+from enum import StrEnum, auto, unique
+from pathlib import Path
+
+import h5py
+import numpy as np
+from pydantic import BaseModel, ConfigDict, Field
+
+_ROOT_PATH = Path(__file__).parent
+_DATA_DIR_PATH = _ROOT_PATH / "data"
+_DATA_FILE_PATH = _DATA_DIR_PATH / "eeg-net.h5"
+
+_SEPARATOR = "=" * 60
+
+
+class _Data(BaseModel):
+    """Container for sample data arrays."""
+
+    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
+
+    target_faces: np.ndarray = Field(..., description="Target face latent vectors")
+    observed_faces: np.ndarray = Field(..., description="Observed face latent vectors")
+    eeg_features: np.ndarray = Field(..., description="EEG feature vectors")
+
+
+# Rebuild model to handle forward references
+_Data.model_rebuild()
+
+
+@unique
+class _RootMetadataKeys(StrEnum):
+    """Root-level metadata keys in the HDF5 file."""
+
+    TITLE = auto()
+    PAPER_TITLE = auto()
+    AUTHORS = auto()
+    YEAR = auto()
+    CONFERENCE = auto()
+    LICENSE = auto()
+    CONTACT_EMAIL = auto()
+
+
+@unique
+class _DatasetMetadataKeys(StrEnum):
+    """Dataset-level metadata keys in the HDF5 file."""
+
+    DESCRIPTION = auto()
+    DIMENSIONS = auto()
+    LATENT_DIM = auto()
+    GAN_MODEL = auto()
+    VALUE_RANGE = auto()
+
+
+def _print_separator(title: str = "") -> None:
+    """Print a formatted separator line.
+
+    Args:
+        title: Optional title to center in the separator
+    """
+    if title:
+        print("\n{}".format(_SEPARATOR))
+        print("{}".format(title).center(60))
+        print("{}".format(_SEPARATOR))
+    else:
+        print("{}".format(_SEPARATOR))
+
+
+def display_dataset_overview(file: h5py.File) -> None:
+    """Display basic dataset information.
+
+    Args:
+        file: Open HDF5 file handle
+    """
+    _print_separator("Dataset Overview")
+
+    print("\nAvailable datasets: {}".format(list(file.keys())))
+    print("Number of samples: {}".format(file.attrs["n_samples"]))
+
+    # Show dataset shapes
+    print("\nDataset shapes:")
+    for key in file.keys():
+        shape = file[key].shape
+        dtype = str(file[key].dtype)
+        size_mb = file[key].nbytes / (1024**2)
+        print(
+            "  {:20s}: {:20s} {:8s} ({:5.1f} MB)".format(
+                key,
+                str(shape),
+                dtype,
+                size_mb,
+            )
+        )
+
+
+def display_metadata(file: h5py.File, max_length: int = 80) -> None:
+    """Display root-level metadata from the HDF5 file.
+
+    Args:
+        file: Open HDF5 file handle
+        max_length: Maximum length for string values before truncation
+    """
+    _print_separator("Metadata")
+
+    for attr in _RootMetadataKeys:
+        attr_value = attr.value
+        if attr_value in file.attrs:
+            value = file.attrs[attr_value]
+            # Truncate long values
+            if isinstance(value, str) and len(value) > max_length:
+                value = value[: max_length - 3] + "..."
+            print("  {:20s}: {}".format(attr_value, value))
+
+
+def _load_sample_data(
+    file: h5py.File,
+    *,
+    n_samples: int = 100,
+) -> _Data:
+    """Load a sample of data for exploration.
+
+    Args:
+        file: Open HDF5 file handle
+        n_samples: Number of samples to load (default: 100)
+
+    Returns:
+        Data container with target_faces, observed_faces, and eeg_features
+    """
+    _print_separator("Data Exploration")
+
+    print("\nLoading first {} samples for exploration...".format(n_samples))
+    target_faces = file["target_faces"][:n_samples]
+    observed_faces = file["observed_faces"][:n_samples]
+    eeg_features = file["eeg_net"][:n_samples]
+
+    print("  Loaded target_faces: {}".format(target_faces.shape))
+    print("  Loaded observed_faces: {}".format(observed_faces.shape))
+    print("  Loaded eeg_features: {}".format(eeg_features.shape))
+
+    return _Data(
+        target_faces=target_faces,
+        observed_faces=observed_faces,
+        eeg_features=eeg_features,
+    )
+
+
+def compute_statistics(
+    *,
+    target_faces: np.ndarray,
+    observed_faces: np.ndarray,
+    eeg_features: np.ndarray,
+) -> None:
+    """Compute and display statistics on the sample data.
+
+    Args:
+        target_faces: Target face latent vectors
+        observed_faces: Observed face latent vectors
+        eeg_features: EEG feature vectors
+    """
+    _print_separator("Data Statistics (first 100 samples)")
+
+    # Face distances (BCI performance metric)
+    distances = np.linalg.norm(target_faces - observed_faces, axis=1)
+
+    print("\nFace distances (target vs observed):")
+    print("  Mean distance:   {:.4f}".format(distances.mean()))
+    print("  Median distance: {:.4f}".format(np.median(distances)))
+    print("  Std distance:    {:.4f}".format(distances.std()))
+    print("  Min distance:    {:.4f}".format(distances.min()))
+    print("  Max distance:    {:.4f}".format(distances.max()))
+
+    # EEG feature statistics
+    print("\nEEG features statistics:")
+    print("  Mean: {:.6f}".format(eeg_features.mean()))
+    print("  Std:  {:.6f}".format(eeg_features.std()))
+    print("  Min:  {:.6f}".format(eeg_features.min()))
+    print("  Max:  {:.6f}".format(eeg_features.max()))
+
+
+def _display_dataset_metadata(
+    file: h5py.File,
+    *,
+    dataset_name: str = "target_faces",
+) -> None:
+    """Display metadata for a specific dataset.
+
+    Args:
+        file: Open HDF5 file handle
+        dataset_name: Name of the dataset to display metadata for
+    """
+    _print_separator("Dataset-Specific Metadata")
+
+    formatted_name = dataset_name.capitalize().replace("_", " ")
+    print("\n{} metadata:".format(formatted_name))
+    ds = file[dataset_name]
+
+    for key in _DatasetMetadataKeys:
+        key_value = key.value
+        if key_value in ds.attrs:
+            value = ds.attrs[key_value]
+            if isinstance(value, str) and len(value) > 60:
+                value = value[:57] + "..."
+            print("  {:15s}: {}".format(key_value, value))
+
+
+def _load_and_explore_dataset(filepath: Path = _DATA_FILE_PATH) -> None:
+    """Orchestrate loading and exploring the dataset.
+
+    This function coordinates all the individual display functions to provide
+    a complete overview of the dataset.
+
+    Args:
+        filepath: Path to the HDF5 data file
+    """
+    _print_separator("Self-Calibrating BCI Dataset (NeurIPS 2025)")
+    print("Loading: {}".format(filepath))
+
+    with h5py.File(str(filepath), "r") as f:
+        # Display basic information
+        display_dataset_overview(f)
+
+        # Display metadata
+        display_metadata(f)
+
+        # Load sample data
+        data = _load_sample_data(f, n_samples=100)
+
+        # Compute and display statistics
+        compute_statistics(
+            target_faces=data.target_faces,
+            observed_faces=data.observed_faces,
+            eeg_features=data.eeg_features,
+        )
+
+        # Display dataset-specific metadata
+        _display_dataset_metadata(f, dataset_name="target_faces")
+
+        _print_separator()
+        print("\n✅ Dataset loaded and explored successfully!")
+        print()
+
+
+def main() -> None:
+    """Main entry point with error handling."""
+    try:
+        _load_and_explore_dataset(_DATA_FILE_PATH)
+    except FileNotFoundError:
+        print("\n❌ Error: {} not found!".format(_DATA_FILE_PATH))
+        print("\nPlease ensure the data file is in the correct location.")
+    except ImportError as e:
+        print("\n❌ Error: Missing required package: {}".format(e))
+        print("\nPlease install required packages:")
+        print("  uv sync  (recommended)")
+        print("  or: pip install ... (check pyproject.toml)")
+    except Exception as e:
+        print("\n❌ Error: {}".format(e))
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "self-calibrating-bci"
+version = "0.1.0"
+description = "Hugging Face data repository for Self-calibrating BCI project (NeurIPS 2025)"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "huggingface-hub>=0.20.0",
+    "numpy>=1.19.0",
+    "pandas>=1.3.0",
+    "pyarrow>=10.0.0",
+    "tqdm>=4.65.0",
+    "click>=8.0.0",
+    "h5py>=3.10.0",
+    "pydantic>=2.12.4",
+]