IMPORTANT UPDATE NOTE (11/30/2023)

There is an unintentional but critical bug in the original code. We appreciate NeuSpeech/EEG-To-Text for pointing it out and providing the fix. The bug is in the eval_decoding.py script where it originally uses teacher forcing for evaluation, but a more appropriate evaluation setting should be using model.generate(). We have updated the codebase to fix this bug with the correction contributed by NeuSpeech. Note that the results from .generate() can be worse than the results shown in the paper.

(AAAI 2022) Open Vocabulary EEG-To-Text Decoding and Zero-shot sentiment classification

Create Environment

run conda env create -f environment.yml to create the conda environment (named "EEGToText") used in our experiments.

Download ZuCo datasets

Download ZuCo v1.0 'Matlab files' for 'task1-SR','task2-NR','task3-TSR' from https://osf.io/q3zws/files/ under 'OSF Storage' root,
unzip and move all .mat files to /dataset/ZuCo/task1-SR/Matlab_files,/dataset/ZuCo/task2-NR/Matlab_files,/dataset/ZuCo/task3-TSR/Matlab_files respectively.
Download ZuCo v2.0 'Matlab files' for 'task1-NR' from https://osf.io/2urht/files/ under 'OSF Storage' root, unzip and move all .mat files to /dataset/ZuCo/task2-NR-2.0/Matlab_files.

Preprocess datasets

run bash ./scripts/prepare_dataset.sh to preprocess .mat files and prepare sentiment labels.

For each task, all .mat files will be converted into one .pickle file stored in /dataset/ZuCo/<task_name>/<task_name>-dataset.pickle.

Sentiment dataset for ZuCo (sentiment_labels.json) will be stored in /dataset/ZuCo/task1-SR/sentiment_labels/sentiment_labels.json.

Sentiment dataset for filtered Stanford Sentiment Treebank will be stored in /dataset/stanfordsentiment/ternary_dataset.json

Usage Example

Open vocabulary EEG-To-Text Decoding

To train an EEG-To-Text decoding model, run bash ./scripts/train_decoding.sh.

To evaluate the trained EEG-To-Text decoding model from above, run bash ./scripts/eval_decoding.sh.

For detailed configuration of the available arguments, please refer to function get_config(case = 'train_decoding') in /config.py

Zero-shot sentiment classification pipeline

We first train the decoder and the classifier individually, and then we evaluate the pipeline on ZuCo task1-SR data.

To run the whole training and evaluation process, run bash ./scripts/train_eval_zeroshot_pipeline.sh.

For detailed configuration of the available arguments, please refer to function get_config(case = 'eval_sentiment') in /config.py

Citation

@inproceedings{wang2022open,
  title={Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification},
  author={Wang, Zhenhailong and Ji, Heng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={5},
  pages={5350--5358},
  year={2022}
}

tanjatang/EEG-To-Text