Re-export at 30s epochs (was 4s windows); update README

README.md

@@ -12,62 +12,74 @@ tags:
 
 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.
 
+> **Update:** this export now stores one **30-second epoch per sample** (previously seven 4-second windows). This matches [`EESM19-Processed`](https://huggingface.co/datasets/Zachary1150/EESM19-Processed) and the canonical sleep-staging unit. See "Preprocessing" below.
+
 ## 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
+- **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` 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
+- 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 sample is retained only when it exists in both modalities
+- recording bounds are checked per 30-second window (the full epoch must lie inside the recording)
+- all signal values are stored as `float32` microvolts at 250 Hz (7,500 samples per window)
 
 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.
 
+### 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. Every
+reference EEG foundation model that does sleep (BENDR, EEGPT, CBraMod, REVE, and the
+EEGPT comparison implementations of LaBraM/BIOT) ingests the full 30-second epoch
+downstream and emits one stage prediction per epoch, rather than splitting one label
+into independently-scored short windows. Storing 30 s is also 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 without re-exporting; the reverse is not possible.
+
 ## 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 |
+| `eesm23-in-ear-eeg.h5` | RB, RT, LB, LT | `(15,523, 4, 7500)` | 1.74 GiB |
+| `eesm23-scalp-eeg.h5` | M1, F3, C3, O1, M2, F4, C4, O2 | `(15,523, 8, 7500)` | 3.48 GiB |
 
-Label distribution:
+`T = 7500` is one 30-second epoch at 250 Hz. `N` is the number of scored epochs, one sample per 30-second AASM epoch.
+
+Label distribution (identical for both modalities — the samples are row-aligned):
 
 | Wake | N1 | N2 | N3 | REM |
 |---:|---:|---:|---:|---:|
-| 9,631 | 9,044 | 51,302 | 17,458 | 21,238 |
+| 1,375 | 1,292 | 7,328 | 2,494 | 3,034 |
 
-## Retained and discarded windows
+## Retained and discarded epochs
 
-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:
+The source scoring files label 17,074 30-second epochs across the 20 sessions (10
+subjects × `ses-001`/`ses-002`): 16,558 carry one of the five retained sleep-stage
+labels and 516 are labeled `Artefact`. Each retained epoch becomes one 30-second
+sample, so the final files contain **15,523** strictly paired samples per modality.
+The exclusions from the 16,558 five-class candidates are:
 
-| Reason | Windows |
+| Reason | Epochs |
 |---|---:|
-| `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.
+| Entire `sub-006/ses-002` pair: truncated/unreadable PSG | 1,031 |
+| 30-second epoch out of bounds or with no cross-modality partner | 4 |
+| **Total excluded** | **1,035** |
+
+(The 516 `Artefact` epochs are outside the five-class task and are never candidates.)
+
+`sub-006/ses-002` is dropped because its scalp PSG file is truncated/unreadable
+(`OSError: could not read bytes`). No sample is excluded because it contains NaN/Inf:
+the files retain 2,230 in-ear and 1,913 scalp samples with at least one non-finite
+value; their indices remain paired even when quality differs between modalities.
 
 ## HDF5 schema (v0.5)
 
 ```text
-/data                 (N, C, 1000) float32
+/data                 (N, C, 7500) float32
 /durations            (N,)         int64
 /nan_fraction         (N,)         float32
 /channel_nan_fraction (N, C)       float32
@@ -87,6 +99,6 @@ quality differs between modalities.
 /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.
+`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. 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`.