--- license: cc0-1.0 tags: - eeg - sleep-staging - in-ear-eeg - scalp-eeg - hdf5 --- # EESM19-Processed Processed HDF5 export of the [EESM19 OpenNeuro dataset](https://doi.org/10.18112/openneuro.ds005185.v1.0.2) ("Ear-EEG Sleep Monitoring 2019", Mikkelsen et al., Aarhus University). It contains paired in-ear EEG and scalp EEG sleep-staging samples from the four home-sleep nights (`ses-001`–`ses-004`) of all 20 subjects — the nights recorded with **simultaneous partial PSG and ear-EEG** on one amplifier. ## Preprocessing Generated with `Ear-EEG-FM-Benchmark/dataset/preprocess_eesm19.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 stored as one 30-second window** (one sample = one epoch = one label = one prediction — the canonical sleep-staging unit) - classes: `Wake`, `N1`, `N2`, `N3`, `REM`; `Artefact`/`Movement`/`Unscored` events are dropped - real sensor/data-loss NaN/Inf samples are preserved and recorded in overall and per-channel quality fields - all signal values are stored as `float32` microvolts at 500 Hz (15,000 samples per window) ### Why 30-second windows (and how to get 4-second windows) The 30-second epoch is the unit at which sleep is scored (AASM) and evaluated: the benchmark's reference foundation models (BENDR, EEGPT, CBraMod, REVE, and the EEGPT comparison implementations of LaBraM/BIOT) all ingest full 30-second epochs downstream and emit one stage prediction per epoch. EEGPT in particular shows a 4-second-pretrained backbone fine-tunes directly on 30-second sleep inputs. We therefore store the full epoch rather than splitting one label into several independently-scored short windows. Storing 30 s loses nothing relative to a shorter export: it is a strict superset. The benchmark loader (`dataset/loader.py`) accepts an `epoch_sec` argument and crops a shorter window from each stored sample at load time, so a 4-second (or any ≤30 s) view is available from these files without re-exporting. The reverse — reconstructing a 30 s epoch from stored 4 s slices — is not possible, which is why the 4-second layout used by `EESM23-Processed` is not used here. 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. A fully-dead channel (all-NaN for a night, e.g. `sub-001/ses-004` F3) is left NaN and flagged in the per-channel quality fields. ### Notes specific to EESM19 (differences from EESM23-Processed) Two things differ from the EESM23 export and are worth reading before use: 1. **Single source file → inherently paired.** On these nights the ear and scalp channels are recorded together in one file, `*_task-sleep_acq-PSG_eeg.set` (there is no separate `acq-earEEG` file — that only exists on the ear-only nights `ses-005+`, which are excluded here). Both modalities are sliced from the same continuous recording on one shared timeline, so every window is present in both — no cross-file pairing/intersection is needed and none is dropped for lack of a partner. 2. **Integer sleep-stage codes with a non-standard mapping.** The scoring column `Scoring1` holds an integer code, not a string stage name, and the mapping (from the dataset's `task-sleep_events.json`) is **not** the usual AASM digit order — note `2 = REM` and `5 = N3`: | code | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |---|---|---|---|---|---|---|---|---| | stage | Wake | REM | N1 | N2 | N3 | A (movement/arousal) | Artefact | Unscored | Codes 6/7/8 are dropped. Labels are re-emitted in the EESM23 class order (`Wake, N1, N2, N3, REM`) so class indices line up across datasets. Only the **first scorer** (`acq-scoring1`) is used; the second scorer's labels are ignored. Channel groups are selected by name (the EEGLAB `.set` marks every channel as `eeg`, so EOG/EMG cannot be told apart by type): 12 ear channels (`ELA ELB ELC ELT ELE ELI ERA ERB ERC ERT ERE ERI`) and 8 scalp channels (`M1 F3 C3 O1 M2 F4 C4 O2`); EOG/EMG are dropped. ## Files | File | Channels | Shape `(N, C, T)` | Size | |---|---|---:|---:| | `eesm19-in-ear-eeg.h5` | ELA, ELB, ELC, ELT, ELE, ELI, ERA, ERB, ERC, ERT, ERE, ERI | `(73,780, 12, 15000)` | 49.50 GiB | | `eesm19-scalp-eeg.h5` | M1, F3, C3, O1, M2, F4, C4, O2 | `(73,780, 8, 15000)` | 33.01 GiB | `T = 15000` is one 30-second epoch at 500 Hz. `N` is the number of scored epochs, not a multiple of it — one sample per 30-second AASM epoch. Label distribution (identical for both modalities — the samples are row-aligned): | Wake | N1 | N2 | N3 | REM | |---:|---:|---:|---:|---:| | 11,084 | 5,421 | 31,508 | 12,497 | 13,270 | ## Retained and discarded epochs Scorer 1 labels 79,058 30-second epochs across the 80 nights (20 subjects × `ses-001`–`ses-004`). Of these, 76,054 carry one of the five retained sleep-stage labels and 3,004 are labeled `Artefact` (codes 6 and 8 — Movement/Unscored — do not occur in this dataset). Each retained epoch becomes exactly one 30-second sample, so the final files contain **73,780** strictly paired samples per modality. The exclusions from the 76,054 five-class candidates are: | Reason | Epochs | |---|---:| | Two source-corrupt sessions with unreadable `.set`/`.fdt` (see below) | 2,119 | | 30-second epoch not fully inside the recording bounds | 155 | | **Total excluded** | **2,274** | (The 3,004 `Artefact` epochs are outside the five-class task and are never candidates, so they are listed separately from the table above.) ### Source-corrupt sessions (2 dropped, 2,119 epochs) Each EEGLAB recording is a pair: a `.set` header (metadata — how long the recording is, how many channels) and a `.fdt` binary holding the raw signal. Two sessions are dropped because **the `.set` header and the `.fdt` binary disagree on the recording length**: the `.fdt` is truncated and contains far fewer samples than the header declares, so the data matrix cannot be reconstructed and MNE refuses to load it (`RuntimeError: Incorrect number of samples`). | Session | `.set` header declares (`pnts` = `len(times)` = `xmax·srate`) | `.fdt` actually contains | Missing | |---|---:|---:|---:| | `sub-011/ses-004` | 17,403,870 samples/ch = **9.67 h** | 3,098,150 = **1.72 h** | ~8.0 h | | `sub-013/ses-001` | 14,515,050 samples/ch = **8.06 h** | 9,094,592 = **5.05 h** | ~3.0 h | For comparison, every intact session matches exactly (e.g. `sub-001/ses-001`: header 14,255,560 = `.fdt` 14,255,560 = 7.92 h). The truncated samples are simply absent from the `.fdt` on OpenNeuro — the local files are byte-identical to the S3 source, so this is source-level corruption, not a download error, and it cannot be repaired. (The intact prefix of each truncated `.fdt` is technically recoverable by bypassing MNE, but that is ~1% of the data and is not attempted here.) This is the same failure class as EESM23's known-corrupt `sub-006/ses-002`. The other 78 sessions (all 20 subjects) load cleanly. ### Out-of-bounds epochs (155 dropped) These are benign, not corruption. A scoring epoch is annotated by an onset time plus a 30-second duration, and a sample is only written when the full 30 s (15,000 points) lies inside the recording. For the last one or two epochs of some nights, `onset + 30 s` runs slightly past the end of the `.fdt` (the recording stops before the final annotated epoch fully elapses), so those epochs are dropped — about two per night across the 78 intact sessions. This is expected: scoring files routinely annotate a little beyond the end of the signal. Because both modalities are sliced from one file on a shared timeline, no sample is excluded for lack of a cross-modality partner. No sample is excluded for containing NaN/Inf: the files retain 38,155 in-ear and 11,399 scalp samples with at least one non-finite value (e.g. a dead channel for a whole night, such as `sub-001/ses-004` F3); their indices remain paired even when quality differs between modalities. ## HDF5 schema (v0.5) ```text /data (N, C, 15000) 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` both identify the source 30-second scoring row; here each sample *is* one epoch, so there is one sample per split group (unlike a 4-second export, where seven windows share a group). For sequence-model sleep staging that needs adjacent-epoch context, group by `subject`+`session` and order by `window_start_sample`. Important attributes include `sfreq`, `class_names`, `unit`, `eegfm_version`, `preprocess_config_json`, `split_group_kind`, and `window_reference`. ## Storage format The signal is stored **uncompressed** as `float32` microvolts at the native 500 Hz, with `/data` chunked one sample per chunk — `chunks = (1, C, 15000)`. This layout is chosen for map-style DataLoader training: each `__getitem__(i)` reads exactly one contiguous chunk (one 30-second epoch with its channels), so random access across the whole file costs one chunk read and no wasted I/O. When the loader crops a shorter `epoch_sec` window it reads only that slice of the chunk. Compression is intentionally **not** applied. EEG windows are high-entropy signals that gzip/lzf shrink only ~1.2–1.5×, and decompression would add CPU cost on every sample fetched by the DataLoader workers; at this scale (tens of GB on local disk) the trade is not worth it. The files are therefore ≈ the raw array size (`N × C × 15000 × 4 bytes`). Note on sample rate: this export keeps the native **500 Hz** (unlike `EESM23-Processed`, which is 250 Hz). Every model in the benchmark resamples to its own expected rate at load time (200 Hz for most; 256 Hz for EEGPT/BENDR), so the stored rate does not affect model inputs — 500 Hz is retained purely to preserve the recording as acquired. Downsampling to 250 Hz would roughly halve the file size with no effect on any model.