# ROAMM: Reading Observed At Mindless Moments

**ROAMM** is a large-scale multimodal dataset featuring simultaneous **EEG and eye-tracking** data collected during naturalistic reading with **span-level mind-wandering annotations**. ROAMM provides a benchmark dataset for MW detection and EEG-to-text decoding tasks, and enables the study of attention-related degradation in language decoding from brain activity in naturalistic reading.

## Dataset Status
* **Synchronized ML Dataset:** For researchers looking for the pre-processed, synchronized EEG and eye-tracking data (Pickle format), please navigate to:
  `derivatives/synced/`
* **Linguistic Content:** Reading materials (words with coordinate information) are stored in `derivatives/stimuli/wiki_stories`. Each word is assigned a unique key to enable mapping fixated words back to their original corpus.
* **Raw EEG (BIDS):** **Work in Progress.** We are currently converting the full raw EEG dataset for all participants into BIDS-compliant format. 

## Project Details
- **Task:** Naturalistic reading of standardized articles with retrospective self-report paradigm (ReMind task).
- **Participants:** 44 subjects (50+ hours of data).
- **Modalities:** - EEG (BioSemi ActiveTwo 64 channels).
    - Simultaneous Eye-Tracking (SR Research EyeLink 1000 Plus).
    - Span-level mind-wandering annotations. 
    - Reading comprehension scores (page-level, multiple-choice questions).

## Structure
This repository follows the Brain Imaging Data Structure (BIDS). 
- `participants.tsv`: Demographic information (age, sex, handedness, ADHD/Reading Disability status).
- `derivatives/synced/`: Synchronized multi-modal data frames ready for Machine Learning pipelines.

## Publication & Citation
The dataset paper describing the collection, synchronization, and baseline modeling of this data will be available online shortly. Once published, please use the citation provided here to credit the work.