ut-vision/EgoBrain-Mini

[ICLR 2026]
EgoBrain: Synergizing Minds and Eyes For Human Action Understanding

EgoBrain-Mini — lightweight, fast, and fully synchronized

東京大学 The Univerisity of Tokyo X 微軟亞洲研究院 Microsoft Research Asia

Nie Lin · Yansen Wang · Dongqi Han · Weibang Jiang · Jingyuan Li · Ryosuke Furuta · Yoichi Sato^* · Dongsheng Li^* · *(Co-corresponding authors)^*

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This is EgoBrain-Mini, a lightweight companion release of our ICLR 2026 paper "EgoBrain: Synergizing Minds and Eyes For Human Action Understanding" — the egocentric subset (first-person video + EEG + IMU + audio), meant as a quick, no-request-needed way to explore the data and its alignment before diving into the full EgoBrain release.

Nie (Elon) Lin, Yansen Wang, Dongqi Han, Weibang Jiang, Jingyuan Li, Ryosuke Furuta, Yoichi Sato*, Dongsheng Li* (*co-corresponding authors). "EgoBrain: Synergizing Minds and Eyes For Human Action Understanding", ICLR 2026.

🗺️ Contents

• 📢 News
• 📦 What's actually in the box
• 📂 Dataset Structure
• ⏱️ How the modalities are aligned
• 🔍 Explore it interactively
• ✅ Verification
• 🔒 Privacy
• 🚫 What's not in this release
• 🚀 Quick start
• ⚙️ Processing
• 🌟 Acknowledgement
• 📜 License

📢 News

• [2026-09-14]: Released Subject P0013 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-12]: Released Subject P0012 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-09]: Released Subject P0011 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-07]: Released Subject P0010 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-06]: Released Subject P0009 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-06]: Released Subject P0008 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-04]: Released Subject P0007 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-03]: Released Subject P0006 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-02]: Released Subject P0005 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-09-02]: Released Subject P0004 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-08-20]: Released Subject P0003 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-08-19]: Released Subject P0002 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-08-19]: Released Subject P0001 egocentric video + EEG + IMU + audio (Phase 2).
• [2026-08-18]: Created the Hugging Face repository for ut-vision/EgoBrain-Mini.

📦 What's actually in the box

Three subjects (P0001, P0002, P0003), 117 real-world sub-actions, four modalities — first-person video, EEG, a body-worn IMU, and audio — all recorded from the same person doing the same thing at the same instant, and individually verified, not sampled.

Modality	File	Format	Shape	Rate / Size	Notes
🧠 EEG (raw)	eeg/raw.npy	float32 array, µV	[32, T]	32 channels @ 256 Hz	Channel-selected only, otherwise unprocessed
🧠 EEG (clean)	eeg/clean.npy	float32 array, standardized	[32, T]	32 channels @ 200 Hz	Detrended, 0.1–75 Hz bandpass + 50 Hz notch, RANSAC bad-channel interpolation, average reference, exponential-moving standardization — see Processing
🖐️ IMU	imu/imu.parquet	Parquet, long format	[T, 20] per device	4 body-worn sensors, rate varies by subject/session — see meta.json's imu_hz, don't assume a fixed number	Left/right wrist + left/right upper arm — see IMU columns
🎙️ Audio	audio/ego.wav	WAV, mono PCM16	[T]	16 kHz	The Ego camera's own microphone
🎥 Video	video/ego.mp4	H.264 / MP4	—	854×480 @ ~30 fps	First-person; a copy of its own mic audio is embedded too, but audio/ego.wav is the higher-fidelity source
📄 Provenance	video/ego.json	JSON	—	—	Exact raw source chapter + frame range this clip was cut from (for traceability; raw video itself is not distributed)

T is the total sample count for that one sub-action's clip — duration_sec × sample_rate — not a separate dimension on top of the rate. For example, the 76.2 s setup-calibration sub-action has eeg/raw.npy.shape == (32, 19508) (76.2 × 256 Hz) and eeg/clean.npy.shape == (32, 15240) (76.2 × 200 Hz exactly). Since duration varies per sub-action (~11s to ~11 min across the 117 of them), T is different for every file — it's never a fixed constant you can hard-code.

The array itself carries no metadata — it's just numbers. To go the other way (shape → duration), either read meta.json's duration_sec directly (the authoritative source, already used in Quick start), or divide back out: duration_sec ≈ T / sample_rate (e.g. 15240 / 200 = 76.2), since the sample rate per file is fixed and documented above.

Every one of the 117 sub-actions has all six of these. Nothing here is a placeholder or a partial modality.

📂 Dataset Structure

EgoBrain-Mini/
├── assets/
├── P0001/
│   ├── manifest.json                      ← index of all 39 segments + metadata
│   ├── segments/
│   │   └── phase2/
│   │       ├── P0001_phase2_000_setup-calibration/
│   │       │   ├── meta.json               ← action label, timestamps, duration, modality flags
│   │       │   ├── eeg/
│   │       │   │   ├── raw.npy
│   │       │   │   └── clean.npy
│   │       │   ├── imu/
│   │       │   │   └── imu.parquet
│   │       │   ├── audio/
│   │       │   │   └── ego.wav
│   │       │   └── video/
│   │       │       ├── ego.mp4
│   │       │       ├── ego.json
│   │       │       └── redaction_windows.json  ← only present where needed, see Privacy below
│   │       └── ...                             (39 segments total, same layout)
│   └── reports/                             ← bundled, offline-first HTML report, see below
├── P0002/                                   ← same layout, 39 segments
├── ...                                      ← more subjects
└── README.md

<segment_id> looks like P0001_phase2_022_drink-water-b — subject, phase, sequence number, and a human-readable action slug.

IMU columns

imu.parquet is long-format: one row per sample, a device column (imu_1…imu_4) selecting which sensor, and a ts column (Unix timestamp):

Column	Meaning
AccX_g, AccY_g, AccZ_g	Acceleration X/Y/Z (g)
GyroX_dps, GyroY_dps, GyroZ_dps	Angular velocity X/Y/Z (degrees/second)
AngleX_deg, AngleY_deg, AngleZ_deg	Orientation angle X/Y/Z (degrees)
MagX_uT, MagY_uT, MagZ_uT	Magnetic field X/Y/Z (µT)
QuatW, QuatX, QuatY, QuatZ	Orientation quaternion
Temperature_C	Sensor temperature (°C)
Battery_pct	Battery level (%)

imu_1 = left wrist, imu_2 = left upper arm, imu_3 = right wrist, imu_4 = right upper arm.

The IMU's own sample rate is not the same across subjects* — P0001's sensors logged at ~198.8 Hz, but P0002's logged at ~9.9 Hz for its entire session (a ~20x difference; same hardware family, evidently a different logging configuration for that session — P0002's raw column names were also formatted differently, see above). Don't hard-code a rate: each segment's meta.json has an imu_hz field computed directly from that file's own ts column, and that's the number to use for any T ↔ duration conversion involving imu.parquet.

_{* During early data collection, the IMU devices for a handful of subjects weren't configured to their maximum output rate (the team was still getting familiar with the hardware) — this is why the main EgoBrain release doesn't include IMU for those subjects at all. EgoBrain-Mini releases it anyway rather than dropping it, since a lower-rate signal is still real, usable data — just be aware of it via imu_hz rather than assuming parity with P0001. We're still looking into whether this can be backfilled or otherwise compensated for in a future update.}

⏱️ How the modalities are aligned

Multimodal synchronization is achieved through an EEG-anchored, cross-modal calibration procedure: EEG is treated as the ground-truth clock, and every other modality's timeline is corrected to match it — not aligned pairwise against each other. The alignment for this subject was independently re-verified against 100% of the published sub-actions (not a sample) before release, including an EEG interval-marker cross-check baked directly into the bundled report's EEG chart (see below).

🔍 Explore it interactively

Each subject's own <subject>/reports/ (e.g. P0001/reports/, P0002/reports/) is a self-contained, offline-first HTML report — no server, no internet connection, no dependencies. Download this repo, then just open <subject>/reports/index.html in a browser:

• A gallery of that subject's 39 sub-actions with thumbnails
• Per-action pages with synced video + live EEG / IMU / audio charts (drag the video, the charts scroll with it, and vice versa — click any chart to jump the video there)
• Plain-language explanations of what each chart actually shows and why

(HF's own file viewer won't execute the JavaScript in these pages — you do need the files on your own machine. That's by design: it means the report works identically whether you're online or not.)

Prefer to browse without downloading first? The Dataset Viewer above (the "Data Studio" / table tab on this page) shows every sub-action across every subject as one row — thumbnail, subject, action label, phase, duration, and which modalities it has — before you commit to downloading anything. It's generated from browse/segments.parquet, a lightweight index (thumbnails only, no video/EEG/IMU payload) built purely for this at-a-glance browsing; the real per-segment data still lives under each subject's own segments/phase2/<segment_id>/ folder as described above.

✅ Verification

Every one of the 117 sub-actions — not a sample — was individually checked:

• EEG integrity: all 32 channels finite, no flat/dead channels
• Alignment: cross-checked against an independent, EEG-hardware-logged marker timestamp
• Coverage: 117 / 117, no segments skipped or excluded from verification

🔒 Privacy

26 of the 117 sub-actions briefly show something that needed redacting — most often a shared device's own lock-screen or an app on a hand-held phone, occasionally a bystander incidentally in frame. Rather than blank out the whole picture for something covering a fraction of it, redaction is region-limited blur: only the specific area of the frame containing the sensitive detail is blurred (strong enough that text, faces, and names are unreadable), for only the seconds it's actually visible — the rest of the frame, and the rest of the clip, is untouched. A small number of windows also mute audio, when the audio itself (not just the picture) needed it. This is baked into the video at the source, not a runtime toggle. video/redaction_windows.json documents exactly which seconds and which region of the frame were affected, for transparency. Nothing else in the release is redacted.

If you come across any content in this release that you believe may reveal a subject's private information, please contact the maintainers immediately at nielin@iis.u-tokyo.ac.jp and it will be re-processed and re-uploaded.

🚫 What's not in this release

• Raw, unprocessed video — not distributed at all; video/*.json retains exact provenance (source file + frame range) in case higher-fidelity access is ever needed for approved use.
• Only Phase 2 (real-world action) sub-actions are included in this subject's data.

🚀 Quick start

import json
import numpy as np
import pandas as pd

root = "P0001/segments/phase2/P0001_phase2_022_drink-water-b"

meta = json.load(open(f"{root}/meta.json"))
eeg_clean = np.load(f"{root}/eeg/clean.npy")        # [32, T] @ 200 Hz
imu = pd.read_parquet(f"{root}/imu/imu.parquet")     # long format, 4 devices

print(meta["action"], meta["duration_sec"], "sec")
print("EEG:", eeg_clean.shape)
print("IMU devices:", imu["device"].unique())

⚙️ Processing

eeg/clean.npy is produced by (in order): linear + 6th-order detrending → 0.1–75 Hz bandpass filter → 50 Hz notch filter → resample to 200 Hz → RANSAC-based bad-channel detection and interpolation → average reference → exponential-moving-average standardization. eeg/raw.npy skips all of this — it's the channel-selected signal at its native 256 Hz, in µV.

🌟 Acknowledgement

We sincerely thank all participants who contributed their time and effort to the data collection process.

This project was initiated and developed during Nie Lin's research internship at Microsoft Research Asia (MSRA), and later continued at the Institute of Industrial Science (IIS), The University of Tokyo. We thank all collaborators and mentors at MSRA and IIS for their valuable guidance and support.

We also acknowledge the support from our advisors, collaborators, and the community.

📜 License

EgoBrain-Mini is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

By using this dataset, you agree to:

• Use the dataset only for non-commercial research purposes
• Not redistribute the data
• Not attempt to identify the participant
• Comply with applicable ethical and data protection regulations

If you have questions regarding licensing or usage, please contact the maintainers at nielin@iis.u-tokyo.ac.jp.

License details: https://creativecommons.org/licenses/by-nc/4.0/