---
license: cc0-1.0
tags:
  - eeg
  - medical-time-series
  - icml2026
  - open-reproductions
task_categories:
  - time-series-forecasting
---

# ADFTD (OpenNeuro ds004504) — processed for the TS-Fingerprint reproduction

Processed arrays for the ICML 2026 reproduction of **"Learning Fingerprints for Medical
Time Series with Redundancy-Constrained Information Maximization"** (OpenReview
[`lrRBHFIgaK`](https://openreview.net/forum?id=lrRBHFIgaK), arXiv
[2605.00130](https://arxiv.org/abs/2605.00130)).

## Provenance

Source: [OpenNeuro ds004504](https://openneuro.org/datasets/ds004504) — *A dataset of EEG
recordings from Alzheimer's disease, Frontotemporal dementia and Healthy subjects*
(Miltiadous et al., 2023), **CC0**. We use the `derivatives/` (ASR + ICA cleaned) `.set`
recordings, 19 channels at 500 Hz, 88 subjects.

## Processing

Reproduces `data_preprocessing/ADFTD_preprocessing.ipynb` of
[DL4mHealth/Medformer](https://github.com/DL4mHealth/Medformer/tree/891f65b8a7e77188508fd8c56e10ccdba7fc1e18)
(the protocol the paper cites), see `prep_adftd.py`:

1. linear-interpolation resample 500 Hz → 256 Hz
2. non-overlapping 256-timestamp (1 s) windows, centre-anchored on each recording
3. labels `{C: 0 healthy, F: 1 FTD, A: 2 AD}`
4. **subject-wise** 60/20/20 split within each class (Medformer `ADFTDLoader`, a=0.6, b=0.8)
5. per-sample, per-channel standardisation (Medformer `uea.normalize_batch_ts`)

## Contents

| File | Shape | Notes |
|---|---|---|
| `X_train.npy` | (40446, 256, 19) | 51 subjects |
| `X_val.npy`   | (14658, 256, 19) | 18 subjects |
| `X_test.npy`  | (14648, 256, 19) | 19 subjects |
| `y_*.npy`     | (N,) int64 | 0 = CN, 1 = FTD, 2 = AD |
| `subj_*.npy`  | (N,) int64 | subject id per window, for leakage checks |

Total **69,752 samples** — this exactly matches the paper's Table 5 (`#Sample = 69,752`),
which is the main evidence that the preprocessing pipeline is faithful.

Please cite the original data descriptor: doi:10.3390/data8060095