license: unknown
tags:
- eeg
- sleep-staging
- in-ear-eeg
EESM23-Processed
Preprocessed HDF5 export of the EESM23 (Aarhus in-ear EEG sleep) BIDS
dataset — 10 subjects, 2 nights each (ses-001, ses-002), one 30-second
AASM scoring epoch per row (Wake / N1 / N2 / N3 / REM; Artefact
epochs dropped). sub-006/ses-002 PSG is skipped — the source .set file is
truncated on disk.
Preprocessing
Only a 0.1–100 Hz band-pass + 50 Hz notch filter is applied, on the full
continuous recording before slicing into 30 s epochs (to avoid per-epoch
filter edge effects). Nothing else: no re-referencing, no resampling, no
channel renaming. Channel names are kept exactly as in the source BIDS
channels.tsv.
Device data-loss gaps (NaN samples) are linearly interpolated before
filtering (a long FIR kernel otherwise smears each NaN across a wide window)
and the true NaN positions are restored afterward, so nan_fraction still
reflects genuine data quality rather than a filtering artifact.
Generated by dataset/preprocess_eesm23.py in the EEGFM repo.
Files
| file | channels | epochs (N) | sfreq | epoch length |
|---|---|---|---|---|
eesm23-in-ear-eeg.h5 |
4: RB, RT, LB, LT (acq=earEEG, original names) | 16553 | 250 Hz | 30.0 s (7500 samples) |
eesm23-scalp-eeg.h5 |
8: M1, F3, C3, O1, M2, F4, C4, O2 (acq=PSG) | 15526 | 250 Hz | 30.0 s (7500 samples) |
Label distribution:
| Wake | N1 | N2 | N3 | REM | |
|---|---|---|---|---|---|
| in-ear | 1501 | 1369 | 7792 | 2667 | 3224 |
| scalp | 1375 | 1293 | 7329 | 2494 | 3035 |
HDF5 schema (v0.2)
/data (N, C, T) float32 signal, µV
/durations (N,) int64 valid samples per epoch (== T here, fixed-length)
/nan_fraction (N,) float32 fraction of non-finite samples in the epoch
/labels (N,) int64 index into attrs['class_names']
/subject (N,) str 'sub-001' ...
/session (N,) str 'ses-001' / 'ses-002'
/task (N,) str 'sleep'
/run (N,) str '' (unused, sessions are not run-qualified)
/trial_id (N,) int64 row index in the source scoring events.tsv
/ch_names (C,) str channel names, as in source BIDS channels.tsv
attrs: sfreq, n_class, class_names, unit ('uV'), eegfm_version,
source_bids_path, bids_dataset_name, preprocess_config_json, created_at
preprocess_config_json (per file) records the exact filter settings, e.g.:
{"acq": "earEEG", "sessions": ["001", "002"], "epoch_sec": 30.0, "filter_low": 0.1, "filter_high": 100.0, "notch": 50.0}