Can Brain Signals Reveal Inner Alignment with Human Languages?
Code for our paper Can Brain Signals Reveal Inner Alignment with Human Languages?.
In EMNLP Findings 2023.
Usage
Set up the Environment
Create a virtual environment and activate it.
python -m venv .env
source .env/bin/activate
Install basic requirements.
pip install -r requirements.txt
Download Datasets
Download K-EmoCon Dataset here.
Download ZuCo Dataset here.
For ZuCo Dataset, please only download task1 and task3.
Preprocessing data
In the preprocessed folder, preprocessed data is readily available for usage. For K-EmoCon, df.csv is used. For ZuCo sentiment analysis, we provide the sentence-level csv in the preprocessed folder.
Preprocessing scripts are provided as well.
Training
The main_new.py file is used for training selected models. Arguments are provided for selecting datasets, modalities, models, levels, and tasks.
Please view the config.py file in tandem and customize it as necessary.
Plotting
The plot.py file is used for plotting TSNE, alignment, and brain topological figures. Arguments are also provided for selecting datasets, modalities, models, levels, tasks, and types of plots. Additionally, plot_new.py is used to plot the learning curves.
Citation
@misc{han2023brain,
title={Can Brain Signals Reveal Inner Alignment with Human Languages?},
author={William Han and Jielin Qiu and Jiacheng Zhu and Mengdi Xu and Douglas Weber and Bo Li and Ding Zhao},
year={2023},
eprint={2208.06348},
archivePrefix={arXiv},
primaryClass={q-bio.NC}
}
Contact
If you have any questions, please contact wjhan@andrew.cmu.edu, jielinq@andrew.cmu.edu.