---
license: cc0-1.0
tags:
  - eeg
  - sleep-staging
  - in-ear-eeg
  - scalp-eeg
  - hdf5
---

# EESM23-Processed

Processed HDF5 export of the [EESM23 OpenNeuro dataset](https://doi.org/10.18112/openneuro.ds005178.v1.0.0). It contains paired in-ear EEG and scalp EEG sleep-staging samples from the scored `ses-001` and `ses-002` recordings of 10 subjects.

## Preprocessing

Generated with `Ear-EEG-FM-Benchmark/dataset/preprocess_eesm23.py` using schema/eegfm version `0.5.0`:

- 0.1–100 Hz band-pass and 50 Hz notch filtering on each continuous recording
- no re-referencing, resampling, or channel renaming
- each labeled 30-second AASM scoring event is split into seven non-overlapping 4-second windows; the final 2 seconds are unused
- classes: `Wake`, `N1`, `N2`, `N3`, `REM`; `Artefact` events are dropped
- real sensor/data-loss NaN/Inf samples are preserved and recorded in overall
  and per-channel quality fields
- the ear-EEG and scalp outputs are strictly row-aligned; a session is excluded
  from both when either modality is missing/unreadable, and a window is retained
  only when it exists in both modalities
- recording bounds are checked separately for every 4-second window
- all signal values are stored as `float32` microvolts at 250 Hz

Brief device data-loss gaps are interpolated before filtering to prevent FIR-kernel contamination, after which the original NaN positions are restored before window selection.

## Files

| File | Channels | Shape `(N, C, T)` | Size |
|---|---|---:|---:|
| `eesm23-in-ear-eeg.h5` | RB, RT, LB, LT | `(108673, 4, 1000)` | 1.66 GiB |
| `eesm23-scalp-eeg.h5` | M1, F3, C3, O1, M2, F4, C4, O2 | `(108673, 8, 1000)` | 3.28 GiB |

Label distribution:

| Wake | N1 | N2 | N3 | REM |
|---:|---:|---:|---:|---:|
| 9,631 | 9,044 | 51,302 | 17,458 | 21,238 |

## Retained and discarded windows

The source scoring files define 119,518 potential 4-second windows, of which
115,906 carry one of the five retained sleep-stage labels and 3,612 are labeled
`Artefact`. The final files contain 108,673 strictly paired windows per
modality. The exclusions are:

| Reason | Windows |
|---|---:|
| `Artefact` label (outside the five-class task) | 3,612 |
| Entire `sub-006/ses-002` pair: truncated/unreadable PSG | 7,217 |
| `sub-002/ses-001`: PSG window has no in-bounds ear-EEG partner | 15 |
| `sub-010/ses-002`: PSG window has no in-bounds ear-EEG partner | 1 |
| **Total excluded, including `Artefact`** | **10,845** |

Among the five-class candidates, 7,233 windows are excluded by recording
availability or strict pairing. No window is excluded because it contains
NaN/Inf. The final files retain 3,491 in-ear windows and 13,328 scalp windows
with at least one non-finite sample; their indices remain paired even when
quality differs between modalities.

## HDF5 schema (v0.5)

```text
/data                 (N, C, 1000) float32
/durations            (N,)         int64
/nan_fraction         (N,)         float32
/channel_nan_fraction (N, C)       float32
/labels               (N,)         int64
/sample_id            (N,)         int64
/subject              (N,)         string
/session              (N,)         string
/task                 (N,)         string
/acquisition          (N,)         string
/run                   (N,)         string
/recording_id          (N,)         string
/trial_id              (N,)         int64
/event_id              (N,)         int64
/split_group_id        (N,)         int64
/window_start_sample   (N,)         int64
/window_stop_sample    (N,)         int64
/ch_names              (C,)         string
```

`event_id` and `split_group_id` identify the source 30-second scoring row. Keep all windows sharing a split group together when constructing train/validation/test partitions.

Important attributes include `sfreq`, `class_names`, `unit`, `eegfm_version`, `preprocess_config_json`, `split_group_kind`, and `window_reference`.