DeWave: Introducing discrete coding into EEG to text translation

Updates: As the baseline methods make a new claim of evaluation MikeWangWZHL/EEG-To-Text#5, we are investigating this problem https://github.com/duanyiqun/DeWave/issues/1 and its potential effects.

Citation

@inproceedings{duan2023dewave,
  title={DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation},
  author={Duan, Yiqun and Zhou, Charles and Wang, Zhen and Wang, Yu-Kai and Lin, Chin-teng},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}

This repo is the implementation of paper xxx which is a discrete encoding (VQ-VAE) into EEG waves to text translation. Please take a look at our paper for more technology details. The overview of the model structure is illustrated below.

This repo is based on the EEG-to-Text codes & implementation.

Data Preparation

• Download raw data from ZuCo
  • Download ZuCo v1.0 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.
• Preparation scripts for eye fixation sliced data
  • Modify the data paths and roots in construct_dataset_mat_to_pickle_v1.py and construct_dataset_mat_to_pickle_v2.py
  • Run scripts/data_preparation_freq.sh for eye-tracking fixation sliced EEG waves.
• Preparation scripts for raw waves
  • Modify the roots variable in scripts/data_preparation_wave.sh and run the scripts, -r suggest the source root, -o denotes the output notes

  ./util/construct_wave_mat_to_pickle_v1.py -t task3-TSR -r /projects/CIBCIGroup/00DataUploading/yiqun/bci -o /projects/CIBCIGroup/00DataUploading/yiqun/bci/ZuCo/dewave_sent

Training

Eye fixation assisted translation

Please refer to train_codex_freq.py and train_translate_freq.py respectively for codex self-supervise initializaiton and translation training. Run scripts scripts/train_codex_ss.sh, scripts/train_translate_codex.sh to train the model.

Direct translation on raw waves

Please notice that the training for raw waves are extremely memory consuming, try larger GPUs Please refer to train_translate_dewave.py for translation training. The self-supervised initialization will be released soon. Run scripts scripts/train_translate_codex_dewave.sh to train the model.

Inference

The model is trained and stored in checkpoints folder. The evaluation scripts is in eval_decoding_freq for eye-tracking fixation and eval_decoding_dewave for raw waves. After evaluation, the text results could be found in results such as example we uploaded.

Results

Generated sample:

	Sample
Ground Truth	Bush attended the University of Texas at Austin, where he graduated Phi Beta Kappa with a Bachelor's degree in Latin American Studies in 1973, taking only two and a half years to complete his work, and obtaining generally excellent grades.
Prediction	was the University of California at Austin in where he studied in Beta Kappa in a degree of degree in history American Studies in 1975. and a one classes a half years to complete the degree. and was a excellent grades.

Due to current training, the model could achieve the best peformance on codex size 2048 and latent size 512. The results are revealed as below.

The subject wise results in task 2.0 on BLEU and ROUGE scores. The text ground truth is the same for each subject, so the metrics difference is not that large. For example we visualize the model trained by subjec id "YMD".

Subject	YDG	YAG	YRP	YLS	YFS	YMD	YRH	YFR	YTL	YAC	YSL	YAK	YMS	YSD	YHS	YDR	YRK	YIS
BLEU-1	44.25	44.25	44.25	44.25	44.05	44.25	44.25	43.41	44.25	44.03	44.45	44.25	44.25	44.25	44.25	44.18	44.25	44.25
BLEU-2	25.83	25.83	25.83	25.83	26.11	25.83	25.83	24.58	25.83	25.55	26.04	25.83	25.83	25.83	25.83	25.86	25.83	25.83
BLEU-3	15.18	15.18	15.18	15.18	15.38	15.18	15.18	14.40	15.18	14.86	15.32	15.18	15.18	15.18	15.18	15.28	15.18	15.18
BLEU-4	8.31	8.31	8.31	8.31	8.34	8.31	8.31	7.76	8.31	7.94	8.45	8.31	8.31	8.31	8.31	8.45	8.31	8.31
ROUGE-R	32.18	32.18	32.18	32.18	31.84	32.18	32.18	31.27	32.18	32.02	32.32	32.18	32.18	32.18	32.18	32.23	32.18	32.18
ROUGE-P	39.34	39.34	39.34	39.34	39.26	39.34	39.34	37.79	39.34	39.29	39.52	39.34	39.34	39.34	39.34	39.28	39.34	39.34
ROUGE-F	35.34	35.34	35.34	35.34	35.11	35.34	35.34	34.16	35.34	35.24	35.50	35.34	35.34	35.34	35.34	35.35	35.34	35.34