| --- |
| license: cc-by-4.0 |
| tags: |
| - eeg |
| - auditory-attention-decoding |
| - event-related-potential |
| - in-ear-eeg |
| - single-word-classification |
| --- |
| |
| # EventAAD-Processed |
|
|
| Minimally preprocessed HDF5 export of **EventAAD Paradigm 3** |
| (`speech~speech~focus`) for single-word auditory-attention decoding. The |
| experiment presents two simultaneous natural-speech streams and asks the |
| listener to attend to one of them. This release contains 24 subjects, 20 |
| experimental trials per subject, and one fixed EEG epoch per selected word. |
|
|
| The two files contain the same 30,739 examples in exactly the same row order, |
| once with scalp EEG and once with in-ear EEG. They can therefore be evaluated |
| separately or paired by row. |
|
|
| ## Task and label construction |
|
|
| This export implements the **single-word Task B** setting from Nguyen et al.: |
| detect an attended target word against words from the unattended speech |
| stream. |
|
|
| Each source `events.tsv` word is anchored at its temporal midpoint |
| (`onset + duration / 2`). Labels are encoded as: |
|
|
| - `1`, `attended`: the word is in the attended stream **and** its event value |
| equals that trial's attended target category (AT). |
| - `0`, `unattended`: the word is in the unattended stream **and** its event |
| value differs from that trial's attended target category. |
|
|
| This yields 2,611 attended and 28,128 unattended epochs, reproducing the class |
| counts reported in the paper's Table I. Twenty-four otherwise eligible |
| unattended epochs are omitted because the complete [-0.2, +1.0] s window would |
| cross a recording boundary. |
|
|
| **Important label-definition note:** the paper describes the negative class |
| in prose as all events in the unattended stream, which could be interpreted as |
| including unattended words whose category matches the attended target (UT). |
| That literal rule produces 28,680 complete negative epochs in the released |
| EventAAD files, not the paper's 28,128. The files here use the table-count- |
| aligned reconstruction above: AT versus the retained unattended non-matching |
| words. This choice is explicit so experiments do not silently conflate the two |
| possible Task B interpretations. |
|
|
| The source word itself and its variable duration determine the epoch center, |
| but **not** the model input length: every example is a fixed 1.201 s signal |
| window from -0.2 s through +1.0 s, inclusive, around the word midpoint. |
|
|
| ## Minimal benchmark preprocessing |
|
|
| The same deliberately small signal-processing front end used by the EEGFM |
| benchmark is applied to each full continuous recording before epoching: |
|
|
| - zero-phase 0.1-100 Hz band-pass; |
| - 50 Hz notch filter (the recordings were collected in Denmark); |
| - retain the native 1000 Hz sampling rate; |
| - no ICA, re-reference, baseline correction, amplitude rejection, resampling, |
| or data augmentation. |
|
|
| Filtering the continuous signal before slicing avoids filter-edge artifacts at |
| each short epoch boundary. Scalp names `T7-T3`, `T8-T4`, `P7-T5`, and `P8-T6` |
| are normalized to `T7`, `T8`, `P7`, and `P8`. The scalp file uses 30 canonical |
| EEG channels and excludes mastoids M1/M2, EOG, and `cRef`; the in-ear file |
| retains all 12 left/right ear electrodes. |
|
|
| ### How this differs from the single-word paper |
|
|
| Nguyen et al.'s task-specific scalp-EEG pipeline used 32 scalp channels, |
| downsampled 1000 Hz recordings to 256 Hz, applied a zero-phase FIR 0.5-40 Hz |
| filter, removed ocular artifacts with ICA, formed [-0.2, +1.0] s epochs around |
| the word midpoint, and rejected epochs exceeding 200 microvolts peak-to-peak. |
| Its training procedure also used random ERP averaging and synthetic ERP |
| augmentation. |
|
|
| Those later steps are intentionally not baked into this release. Keeping the |
| native rate and a common broad passband makes this dataset comparable with |
| other EEGFM benchmark datasets and leaves model-specific time/frequency |
| transforms to the downstream pipeline. Avoiding ICA and amplitude rejection |
| also prevents task-specific cleaning choices and modality-dependent row loss, |
| so scalp and ear examples remain paired. Augmentation belongs inside the |
| training split and should never be applied to validation or test examples. |
|
|
| ## Files |
|
|
| | file | channels | examples (N) | sfreq | epoch | |
| |---|---:|---:|---:|---:| |
| | `eventaad-task-b-scalp-eeg.h5` | 30 scalp EEG | 30,739 | 1000 Hz | 1.201 s (1201 samples) | |
| | `eventaad-task-b-in-ear-eeg.h5` | 12 in-ear EEG | 30,739 | 1000 Hz | 1.201 s (1201 samples) | |
|
|
| Both files contain 28,128 label-0 and 2,611 label-1 rows. There is one |
| Paradigm-3 recording/session per subject and 480 experimental trials in total |
| (24 subjects x 20 trials). `trial_id` identifies the actual parent experimental |
| trial (`0` through `19`), `event_id` identifies the source `events.tsv` word |
| row, and `sample_id` identifies the processed HDF5 row. `split_group_id` equals |
| `trial_id` because a trial is the indivisible split unit. |
|
|
| ### Leakage-safe protocol grouping |
|
|
| Word epochs from the same continuous experiment are not independent: nearby |
| words can share most of their source EEG samples. Split by |
| `(recording_id, split_group_id)`, never by individual HDF5 row. The benchmark's |
| default multi-subject and within-subject temporal split keeps trials 0-13 in |
| train, 14-16 in validation, and 17-19 in test. This gives 21,427 / 4,368 / |
| 4,944 word epochs respectively, with both classes in every subject-level |
| partition and zero shared source samples across partitions. LOSO instead keeps |
| the held-out subject intact. |
|
|
| `window_start_sample` and `window_stop_sample` provide half-open source- |
| recording bounds for independently auditing overlap. If custom fractions put a |
| boundary between adjacent experimental trials whose word windows overlap, |
| those trials must be merged into one indivisible connected group. |
|
|
| ## HDF5 schema (v0.4) |
|
|
| ``` |
| /data (N, C, T) float32 signal, microvolts |
| /durations (N,) int64 valid samples per epoch (1201 here) |
| /nan_fraction (N,) float32 fraction of non-finite samples in the epoch |
| /labels (N,) int64 index into attrs['class_names'] |
| /sample_id (N,) int64 processed HDF5 row identity |
| /subject (N,) str 'sub-001' ... |
| /session (N,) str empty (no separate BIDS session entity) |
| /task (N,) str 'speech~speech~focus' |
| /acquisition (N,) str empty (both channel subsets share one recording) |
| /run (N,) str empty (no separate BIDS run entity) |
| /recording_id (N,) str continuous source-recording identity |
| /trial_id (N,) int64 experimental trial, 0...19 |
| /event_id (N,) int64 row index in the source events.tsv |
| /split_group_id (N,) int64 equals trial_id |
| /window_start_sample (N,) int64 start in source recording |
| /window_stop_sample (N,) int64 exclusive stop in source recording |
| /ch_names (C,) str channel names |
| |
| attrs: sfreq, n_class, class_names (['unattended','attended']), unit ('uV'), |
| eegfm_version, source_bids_path, bids_dataset_name, |
| preprocess_config_json, split_group_kind ('trial'), |
| trial_id_kind ('experimental_trial'), |
| event_id_kind ('source_events_tsv_row'), sample_id_kind, |
| window_reference, created_at |
| ``` |
|
|
| `preprocess_config_json` records the exact channel list, label rule, filter |
| settings, epoch window, and the preprocessing operations intentionally left |
| unset. |
|
|
| ## Data-quality note |
|
|
| This is a minimal-processing benchmark export, not a manually curated clinical |
| dataset. Some source in-ear channels contain extreme-amplitude segments. They |
| are retained rather than silently removing epochs or breaking scalp/ear row |
| alignment. Downstream experiments should fit any robust scaling or quality- |
| control thresholds on the training partition only and report those choices. |
|
|
| ## Download |
|
|
| ```bash |
| hf download Zachary1150/EventAAD-Processed --repo-type dataset \ |
| --local-dir EventAAD-Processed |
| ``` |
|
|
| ## Sources |
|
|
| - Original data: [EventAAD on Zenodo](https://zenodo.org/records/8082867), |
| released under CC BY 4.0. |
| - Dataset paper: Geirnaert et al., *Cognitive component of auditory attention |
| to natural speech events*, Frontiers in Human Neuroscience (2024), |
| [doi:10.3389/fnhum.2024.1460139](https://doi.org/10.3389/fnhum.2024.1460139). |
| - Single-word task: Nguyen et al., *Single-word Auditory Attention Decoding |
| Using Deep Learning Model* (2024), |
| [arXiv:2410.19793](https://arxiv.org/abs/2410.19793). |
|
|
| When using these processed files, please cite the original EventAAD dataset and |
| the relevant task paper in addition to this repository. |
|
|
