| --- |
| license: cc-by-nc-4.0 |
| viewer: false |
| extra_gated_prompt: >- |
| This dataset is provided for academic research and NeuroMM-2026 challenge participation only. |
| By requesting access, your team confirms that all submitted information is accurate and complete. |
| The dataset, annotations, and any derived files must not be redistributed, mirrored, modified, or used for commercial purposes without prior written permission. |
| Access will be granted only after manual review by the NeuroMM-2026 organizers. |
| extra_gated_fields: |
| Team Name: text |
| Team Leader Name: text |
| Team Leader Email: text |
| Team Members (comma-separated): text |
| Organization / University / Company: text |
| Country / Region: country |
| I confirm that my team will use this dataset for academic research and NeuroMM-2026 challenge participation only: checkbox |
| I agree not to redistribute the dataset, annotations, or derived files without written permission: checkbox |
| language: |
| - en |
| tags: |
| - eeg |
| - multimodal |
| - seizure-detection |
| - benchmark |
| - neuromm-2026 |
| --- |
| |
| # NeuroMM-2026 — Train + Val Open Release |
|
|
| This is the **train + val partition** of the NeuroMM-2026 multimodal seizure detection challenge dataset. |
| It contains **25,426 EEG samples** (20,298 train / 5,128 val) and matching pre-extracted **visual features** from 7 vision backbones. |
| **Test data is not included** and will be evaluated by the organizers on a private leaderboard. |
|
|
| Challenge website: <https://2026.neuromm.org> |
|
|
| --- |
|
|
| ## Dataset Access Form |
|
|
| Please **follow this format before submitting the gated form**. Many requests are rejected because the team information does not match the expected format. |
|
|
| ### Example Application |
|
|
| | Field | Example | |
| | :--- | :--- | |
| | Team Name | GML-MM-Lab | |
| | Team Leader Name | Alice Chen | |
| | Team Leader Email | alice.chen@university.edu | |
| | Team Members (comma-separated) | Alice Chen, Bob Li, Carol Wang | |
| | Organization / University / Company | Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) | |
| | Country / Region | China | |
|
|
| - Submit **one request per team**, not one request per member. |
| - The request should be submitted by the **team leader or the main contact person**. |
| - Make sure the team name and member list are consistent with your challenge registration. |
|
|
| --- |
|
|
| ## Tasks |
|
|
| - **Task 1** — Binary spike-vs-non-spike classification (EEG only) |
| - **Task 2** — Binary spike classification (EEG + Video) |
| - **Task 3** — 5-class seizure subtype classification (positives only, EEG + Video) |
|
|
| ## Layout |
|
|
| ``` |
| NeuroMM-2026/ |
| ├── README.md ← this file |
| ├── annotations/ |
| │ └── neuromm2026_train_val.csv ← 25,426 rows (20,298 train / 5,128 val) |
| ├── splits/ |
| │ └── split.md ← patient-level partition documentation |
| └── archives/ |
| ├── eeg.tar ← 25,426 raw EEG .npy |
| ├── video_clip-base.tar ← OpenAI CLIP ViT-B/32 features (8, 512) |
| ├── video_videomae-base.tar ← VideoMAE-base features (1, 768) |
| ├── video_videomae-large.tar ← VideoMAE-large features (1, 1024) |
| ├── video_dinov2-base.tar ← DINOv2-base features (8, 768) |
| ├── video_dinov2-large.tar ← DINOv2-large features (8, 1024) |
| ├── video_siglip-base.tar ← SigLIP-base features (8, 768) |
| └── video_timesformer-k400.tar ← TimeSformer (Kinetics-400) (1, 768) |
| ``` |
|
|
| After extraction: |
| ``` |
| tar -xf archives/eeg.tar |
| # -> processed/features/eeg/{sample_id}.npy |
| tar -xf archives/video_clip-base.tar |
| # -> processed/features/video/clip-base/{sample_id}.npy |
| ``` |
|
|
| ## Manifest Columns |
|
|
| | Column | Description | |
| |---|---| |
| | `sample_id` | Unique window identifier (matches the `.npy` filename stem) | |
| | `split` | `train` (20,298) or `val` (5,128); patient-disjoint | |
| | `label` | Binary label: 1 = spike / seizure positive, 0 = negative | |
| | `label_type` | Multi-class label: 0 = negative, 1–5 = seizure subtype | |
| | `subject_id` | Patient identifier; use to enforce patient-disjoint cross-validation | |
|
|
| ## EEG Feature Format |
|
|
| Each `.npy` is a NumPy array shape `(29, 2000)` (mixed `float16` / `float32`): |
| - 29 raw EEG channels at 500 Hz |
| - 2000 timesteps = 4-second window |
|
|
| The reference baseline derives 23 + 3 ECG/EMG = 26 channels via differential pairs. See the official baseline repository for the loader. |
|
|
| ## Loading Example |
|
|
| ```python |
| import numpy as np, pandas as pd |
| df = pd.read_csv("annotations/neuromm2026_train_val.csv") |
| print(df["label_type"].value_counts()) # multi-class distribution |
| sid = df.iloc[0]["sample_id"] |
| x = np.load(f"processed/features/eeg/{sid}.npy") |
| print(x.shape, x.dtype) # (29, 2000) |
| |
| # Video feature for the same sample (CLIP-base) |
| v = np.load(f"processed/features/video/clip-base/{sid}.npy") |
| print(v.shape, v.dtype) # (8, 512) |
| ``` |
|
|
| ## Important: Patient-Level Splitting |
|
|
| The provided train/val split is **patient-disjoint** (no patient appears in both). If you do additional CV folds, also enforce patient-level splitting via `subject_id` to avoid label leakage. |
|
|
| ## License |
|
|
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — academic research and NeuroMM-2026 challenge participation only. No redistribution, no commercial use. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @dataset{neuromm2026, |
| title = {NeuroMM-2026: Multimodal Seizure Detection Dataset}, |
| year = {2026}, |
| url = {https://2026.neuromm.org} |
| } |
| ``` |
|
|
|  |
|
|
