ReadingEmebdding - Implementation

From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking

Accepted to IEEE-EMBC 2024

Paper : https://arxiv.org/pdf/2401.15681.pdf

Abstract: Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain.

This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training that integrates EEG and eye-tracking biomarkers through an attention-based encoder.

This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level.

Requirements

Implemented in Python3.10 with the following key packages:

pytorch = 2.0.1
scikit-learn = 2.1.2
numpy = 1.25.0
scipy = 1.10.1

# For plotting
matplotlib
seaborn

Datasets

Pre-processed from ZuCo 1.0: Google Drive Download and keep them in ./Datasets/

Usage (Will be updated with easier hyper-parameter settings)

• trainREmodel.py to train the model on the datasets
• CV_REmodel.py to perform K-fold cross validation on the datasets

Sample SLURM script provided in script_train.sh if needed to run on a cluster

• TransformerClassifier_REmbedding.ipynb for an overview of code in an all-in-one style

Citation

Cite using the Bibtex citation below

@article{zhang2024word,
  title={From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking},
  author={Zhang, Yuhong and Yang, Shilai and Cauwenberghs, Gert and Jung, Tzyy-Ping},
  journal={arXiv preprint arXiv:2401.15681},
  year={2024}
}