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NeuroMM
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NeuroMM-2026

Languages:
English
Tags:
eeg
multimodal
seizure-detection
benchmark
neuromm-2026
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metadata 
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 

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 — academic research and NeuroMM-2026 challenge participation only. No redistribution, no commercial use.

Citation

If you use this dataset, please cite:

@dataset{neuromm2026,
  title  = {NeuroMM-2026: Multimodal Seizure Detection Dataset},
  year   = {2026},
  url    = {https://2026.neuromm.org}
}

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