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

hf download Zachary1150/EventAAD-Processed --repo-type dataset \
  --local-dir EventAAD-Processed

Sources

• Original data: EventAAD on Zenodo, 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.
• Single-word task: Nguyen et al., Single-word Auditory Attention Decoding Using Deep Learning Model (2024), arXiv:2410.19793.

When using these processed files, please cite the original EventAAD dataset and the relevant task paper in addition to this repository.