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