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Additional Data

Participant metadata and pipeline-generated trial summaries for the REPLAY analysis.

Files

participants_info.xlsx

Master participant registry — one row per recording session. Read by Things_Importer.get_participant_info() (utils/imports.py) via pandas.read_excel(engine="calamine", decimal=",").

Columns:

Column Description
Participant Unique participant code (e.g. BabyPilot12)
Baby 1 if an infant participant, 0 for adult/pilot test recordings (excluded by default via baby_only)
Sub_Code Internal lab subject code
Date Session/testing date
Time Session start time
Cap_Size EEG cap size used
Birthday Participant date of birth
Gender m / f
Video_useable 1/0 — whether behavioral video coding is usable
Video_Notes Free-text notes on the video coding
Localizer_usable 1/0 — whether the localizer segment passed QC
Sequence_usable 1/0 — whether the sequence-learning segment passed QC
Signal_Quality Categorical EEG signal-quality rating
Include 1/0 master inclusion flag
EOG 1/0 — whether an EOG channel is usable/available
General_Notes Free-text notes

Age in months is not stored directly — it is derived on the fly from Birthday and Date (leap-year corrected) inside get_participant_info(), then used to split participants into the old (~10–13 months) and young (~6–9 months) groups, each with independently configurable filters (baby_only, include_only, min_age_months, max_age_months, require_localizer, require_sequence).

Usage:

from utils.imports import Things_Importer
from utils.paths import INFO_DIR, PREPROC_DIR

importer = Things_Importer(INFO_DIR, PREPROC_DIR)

# Old group (defaults: baby_only=True, include_only=True, 10-13 months, localizer+sequence required)
participant_info_old = importer.get_participant_info()

# Young group (override only the age range; other filters keep their defaults)
participant_info_young = importer.get_participant_info(
    participant_filters={"min_age_months": 5, "max_age_months": 10}
)

trial_info_old.csv / trial_info_young.csv

Generated outputs, one per age group, written by get_additional_data() (utils/imports.py) each time load_data.py runs. Checked into the repo as a snapshot of the current dataset — not required as pipeline input.

Columns:

Column Description
Participant Sequential index (1, 2, …) among included participants in this group — not the Participant code from participants_info.xlsx
Age Age at testing in months
Gender m / f
Localizer_Trials Number of localizer trials retained after preprocessing/rejection
Localizer_Trials_Percent Localizer_Trials / 540 (540 = trials presented)
Sequence_Presentations Number of retained sequence-learning presentations
Sequence_Presentations_Percent Sequence_Presentations / 100 (100 = presentations shown)

Usage:

import pandas as pd

trial_info_old = pd.read_csv("additional_data/trial_info_old.csv")
trial_info_young = pd.read_csv("additional_data/trial_info_young.csv")

Data Location

The preprocessed EEG segment files and raw attention-rating files referenced by utils/imports.py are stored separately (not in this repo) and must be placed under the RAW_DIR / PREPROC_DIR paths configured in utils/paths.py:

<PREPROC_DIR>/
└── Segments/
    ├── Localizer/<Participant>_Epochs.fif
    ├── Resting/<Participant>_Epochs.fif
    ├── CuedReplay/<Participant>_Epochs.fif
    ├── LearnSequence/<Participant>_Epochs.fif
    └── PreResting/
        ├── <Participant>_Epochs.fif
        └── <Participant>_Break_Epochs.fif      # merged with the file above into "preresting"

<RAW_DIR>/
└── ratings/
    ├── rater1/Template_Localizer_<Participant>.csv     # ";" separated
    └── rater2/Template_Localizer_<Participant>.xlsx     # read via engine="calamine"