--- pretty_name: OLIVE Physiological Dataset (EEG + Pupil) license: cc-by-4.0 tags: - eeg - fixation-related-potentials - pupillometry - eye-tracking - xr - bci - attention size_categories: - 10K=0); a per-BLOCK label, not per-study | `block_idx` caveat: `iter_epochs` does not carry a literal block index field. `run` was verified empirically (against on-disk `meta.jsonl`/block-directory counts for multiple subjects across us1/us2/us3) to be a 1-indexed running block counter, so `block_idx = run - 1` is used. This has not been verified for every subject/session; treat `block_idx`/`difficulty` as best-effort, not a guaranteed-exact join. `task` is derived from `difficulty` (`difficulty == -1` => `visual_search`, else `spaceshooter`) and therefore inherits the same best-effort/fallback behavior: a failed metadata lookup falls back to `difficulty = -1` => `task = visual_search`. ## EEG montage and epoch windows - **EEG**: 20-channel B-Alert X24 subset, standard 10-20 layout, sampled at 256 Hz. Epoch window is fixation-onset-locked `[-0.1, 0.8]` s → 230 samples/channel. - **Pupil**: 2-channel (left/right) pupil diameter, sampled at 20 Hz. Epoch window is fixation-onset-locked `[-1.0, 3.0]` s → 80 samples/channel. - Both windows match `release/dataset/extract_epochs.py`'s `EEG_N_T_DEFAULT` / `PUPIL_N_T_DEFAULT` constants (reused, not redefined, here). ## Condition legend - `condition` (primary, as-published): `IE` = Implicit+Explicit (EEG + shot events), `E` = Explicit-only (shot events, no EEG). This is the label used throughout the paper's published tables and figures. - `condition_ra`: a secondary v1 RA-proposed relabeling overlay (adds `Oracle` / `Control` labels for a handful of subjects) that is **UNRESOLVED against the published cohort and does not match the paper's tables** (see `release/dataset/attach_metadata.py`'s module docstring and `_RA_OVERRIDES`). Included for transparency/auditing only; always prefer `condition` for anything that should agree with the paper. ## `p_target` generation `p_target` is the per-fixation implicit-evidence target probability produced by the default decoder in `release/olive/decode.py` (`DefaultDecoder`), which reads per-subject parameters under `eeg_priors/`. It is `NaN` for subjects/rows without available parameters. Replace the decoder with your own; see the repository README. ## Coverage Built from subjects=[4, 5, 12, 18, 20, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 46, 47, 48, 49, 51, 52, 53, 54, 55, 59], studies=['us1', 'us2', 'us3'], with_saccades=False. - Total examples: 58184 - Target-label rate (`y==1`): 0.4035 (23478 target / 34706 non-target) - `p_target` valid (non-NaN): 18246 rows, mean=0.5059; NaN: 39938 rows - `p_target_quality` (valid rows): mean=0.7669, range=[0.5375, 0.8851] - Subjects with zero examples across all studies: 0 ([]) - Subjects with >0 examples in at least one study: 25 ([4, 5, 12, 18, 20, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 46, 47, 48, 49, 51, 52, 53, 54, 55, 59]) Full per-subject x per-study counts: see `coverage.csv` (same directory as this card). Table (subject_id x study, `total` = row sum): | subject_id | us1 | us2 | us3 | total | |---|---|---|---|---| | 4 | 725 | 925 | 1046 | 2696 | | 5 | 591 | 0 | 0 | 591 | | 12 | 564 | 0 | 863 | 1427 | | 18 | 796 | 745 | 0 | 1541 | | 20 | 1071 | 1407 | 994 | 3472 | | 28 | 840 | 867 | 863 | 2570 | | 29 | 633 | 901 | 1025 | 2559 | | 31 | 520 | 0 | 0 | 520 | | 33 | 662 | 598 | 560 | 1820 | | 34 | 997 | 1109 | 1305 | 3411 | | 35 | 778 | 1023 | 1217 | 3018 | | 36 | 1022 | 1332 | 1466 | 3820 | | 37 | 1014 | 932 | 1026 | 2972 | | 39 | 1226 | 1755 | 1609 | 4590 | | 40 | 875 | 693 | 795 | 2363 | | 46 | 783 | 0 | 1217 | 2000 | | 47 | 612 | 0 | 0 | 612 | | 48 | 1412 | 2049 | 1593 | 5054 | | 49 | 759 | 1961 | 1488 | 4208 | | 51 | 1049 | 1332 | 798 | 3179 | | 52 | 607 | 0 | 0 | 607 | | 53 | 1013 | 1473 | 0 | 2486 | | 54 | 946 | 0 | 0 | 946 | | 55 | 924 | 0 | 0 | 924 | | 59 | 798 | 0 | 0 | 798 | ## Saccade fields caveat (`with_saccades`) `saccade_*` and `fixation_duration` are **all-NaN by default** (`with_saccades=False`). Deriving them requires loading a session's raw `.p` eye-tracking recording (up to ~1-3GB) and joining each epoch's TFRecord-clock `fix_time_s` against `long_gaze.jsonl`'s LSL-clock `t_onset` by nearest-match within `item_id`, subject to a tolerance (default 1.0s); **these two clocks are not guaranteed to share an origin**, so even with `with_saccades=True`, a session whose clocks do not align will legitimately yield all-NaN saccade fields for every epoch in that session; this is a documented limitation of the join, not a bug. A second caveat: for the small number of subjects with multiple numbered "session" subdirectories under one study directory but only one physical `.p` file at the study-directory root (observed for subject 5 / us1), the join falls back to that one shared `.p` for every session under that study directory, which can pool fixation events across sessions. See `release/dataset/export_hf.py`'s module docstring for full detail. ## Consent All participants in the published cohort provided informed consent under the study's IRB protocol for their de-identified physiological (EEG, pupil), behavioral, and gaze data to be included in a public research dataset release. No directly identifying information (name, contact info, raw video) is included in this export; `subject_id` is a study-internal integer id, not a real-world identifier. ## License **TBD**: placeholder, CC-BY-4.0 (pending final confirmation from the study PI / IRB before public release). ## Citation **Placeholder**: update with the final paper citation before public release: ``` @inproceedings{olive-physio-2026, title = {OLIVE: [paper title TBD]}, author = {[authors TBD]}, booktitle = {[venue TBD]}, year = {2026}, } ``` ## ERN variant A second HuggingFace config (`ern`), loadable via `load_dataset("ApocalyVec/olive-physio", "ern")`, of per-shot response-locked ERN (error-related negativity) epochs, distinct from the fixation-locked FRP epochs in the `default` config above. ### What this is One row per in-game shot event (enemy hit = correct, friendly-fire hit = error) from the OLIVE Wingman / SpaceShooter user studies (US1, US2, US3), for the release cohort of 25 participants. Each row is a single-trial, response-locked EEG epoch around the shot event, labeled correct/error. ### Fields (one row per shot event) | field | type | description | |---|---|---| | `subject_id` | int | participant id | | `study` | str | `us1` / `us2` / `us3` | | `session` | str | recording-session `.p` file stem | | `eeg` | float32[20, 205] | response-locked EEG epoch, uV, see window below | | `label` | int (0/1) | `0` = correct (enemy hit), `1` = error (friendly-fire hit) | | `shot_time` | float | LSL timestamp of the shot event | | `montage` | list[str] | B-Alert channel names, in `eeg` row order | | `condition` | str | primary, as-published condition label (`IE` / `E`), same as the `default` config's `condition` field; **use this for any published-table-facing analysis** | ### Epoch window and filtering - **EEG**: 20-channel B-Alert X24 subset (same montage as the FRP config), sampled at 256 Hz. - **Window**: response-locked (shot-event-locked) `[-200, 600]` ms → 205 samples/channel. - **Filter**: continuous zero-phase 4th-order Butterworth bandpass, 0.5-30.0 Hz, applied to the full continuous recording *before* epoching (per-epoch filtering of an ~800 ms window is invalid for a 0.5 Hz high-pass, which needs several seconds of settling). - **Baseline window**: `[-200, 0]` ms (pre-response) is the conventional ERN baseline period included in the epoch; the exported `eeg` array is the filtered epoch as-is and is **not baseline-corrected**; apply baseline correction (subtract the `[-200, 0]` ms mean per channel) yourself if your analysis requires it. - **Label convention**: shot events come from `Unity.ReNa.EventMarkers` row 2 (DTN-coded); DTN==1 (friendly fire) → `label=1` (error), DTN==2 (enemy) → `label=0` (correct). Verified identical across US1/US2/US3 (see `release/dataset/ern/extract_ern.py`'s module docstring for the per-subject verification counts). ### Availability Available for all three studies: US1 (offline simulation), US2 (live deployment), US3 (silent target-switch). Coverage varies by subject/study; some subjects have zero epochs in a given study (no session recorded, or no `.p` file with usable shot events); see the coverage table below and `ern_coverage.csv` (same directory as this card) for exact per-subject counts. ### Coverage Built from subjects=[4, 5, 12, 18, 20, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 46, 47, 48, 49, 51, 52, 53, 54, 55, 59], studies=['us1', 'us2', 'us3']. - Total examples: 29178 - Error rate (`label==1`): 0.2352 (6864 error / 22314 correct) - Subjects with zero examples across all studies: 1 ([47]) - Subjects with >0 examples in at least one study: 24 ([4, 5, 12, 18, 20, 28, 29, 31, 33, 34, 35, 36, 37, 39, 40, 46, 48, 49, 51, 52, 53, 54, 55, 59]) Full per-subject x per-study correct/error counts: see `ern_coverage.csv`. Table (subject_id x study, `total` = row sum): | subject_id | us1_correct | us1_error | us2_correct | us2_error | us3_correct | us3_error | total | |---|---|---|---|---|---|---|---| | 4 | 218 | 100 | 404 | 141 | 483 | 118 | 1464 | | 5 | 224 | 120 | 0 | 0 | 0 | 0 | 344 | | 12 | 158 | 80 | 0 | 0 | 0 | 0 | 238 | | 18 | 190 | 85 | 327 | 199 | 0 | 0 | 801 | | 20 | 162 | 81 | 388 | 190 | 412 | 117 | 1350 | | 28 | 258 | 103 | 416 | 149 | 502 | 137 | 1565 | | 29 | 291 | 143 | 544 | 215 | 651 | 148 | 1992 | | 31 | 300 | 137 | 0 | 0 | 0 | 0 | 437 | | 33 | 246 | 109 | 613 | 123 | 513 | 106 | 1710 | | 34 | 362 | 89 | 683 | 111 | 573 | 115 | 1933 | | 35 | 300 | 113 | 377 | 57 | 226 | 31 | 1104 | | 36 | 427 | 95 | 697 | 120 | 687 | 85 | 2111 | | 37 | 381 | 89 | 714 | 152 | 684 | 109 | 2129 | | 39 | 273 | 123 | 651 | 232 | 566 | 210 | 2055 | | 40 | 264 | 66 | 0 | 0 | 540 | 105 | 975 | | 46 | 316 | 134 | 0 | 0 | 566 | 213 | 1229 | | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 48 | 305 | 109 | 627 | 228 | 605 | 165 | 2039 | | 49 | 92 | 43 | 563 | 247 | 573 | 193 | 1711 | | 51 | 229 | 84 | 438 | 142 | 392 | 140 | 1425 | | 52 | 182 | 65 | 0 | 0 | 0 | 0 | 247 | | 53 | 279 | 81 | 676 | 198 | 0 | 0 | 1234 | | 54 | 313 | 114 | 0 | 0 | 0 | 0 | 427 | | 55 | 240 | 119 | 0 | 0 | 0 | 0 | 359 | | 59 | 213 | 86 | 0 | 0 | 0 | 0 | 299 |