(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}
}