IMPORTANT UPDATE (3/25/2024)

The code is updated following NeuSpeech.

We thank Yiqian Yang for fixing the bugs in the original code and providing the new metrics!

Please refer to https://github.com/NeuSpeech/EEG-To-Text/tree/master for the latest version of the code (be sure to clone the master branch).

git clone -b master https://github.com/NeuSpeech/EEG-To-Text.git

Please also check out their recent paper on decoding brain signals: https://arxiv.org/abs/2403.01748

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what's new?

NeuSpeech add teacher-forcing and non-teacher forcing, input_noise and not-input-noise conditions in evaluation

fix bugs in original code, so you can run without debugging.

note: new metrics used torchmetrics, which may be different from original one. results and metrics are in ./results folder

as for the new implementation, input noise can get higher or equal scores on bleu, which means the decoding is not effective! We strongly suggest everyone when doing brain decoding text, you should compare your results with input noise! results

noise as input	teacher-forcing	bleu-1	rouge-1f
yes	yes	27.47	33.62
no	yes	27.84	33.77
yes	no	9.23	13.99
no	no	8.87	13.56

(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 ~/datasets/ZuCo/task1-SR/Matlab_files,~/datasets/ZuCo/task2-NR/Matlab_files,~/datasets/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 ~/datasets/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 ~/datasets/ZuCo/<task_name>/<task_name>-dataset.pickle.

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

Sentiment dataset for filtered Stanford Sentiment Treebank will be stored in ~/datasets/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}
}