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DBPNet

Code for paper: DBPNet: Dual-Branch Parallel Network with Temporal-Frequency Fusion for Auditory Attention Detection

This paper introduces DBPNet, a novel AAD network that integrates temporal and frequency domains to enhance EEG signal decoding.

Qinke Ni, Hongyu Zhang, Shengbing Pei, Chang Zhou, Zhao Lv, Cunhang Fan. DBPNet: Dual-Branch Parallel Network with Temporal-Frequency Fusion for Auditory Attention Detection. In Ijcai 2024.

Preprocess

  • Please download the AAD dataset for training.
  • The public KUL dataset, DTU dataset and MM-AAD(not yet open) are used in this paper.

Requirements

  • Python3.11.4
    pip install -r requirements.txt

Run

  • Modify the args.* variable in model.py to match the dataset
  • Using model.py to train and test the model
  • Using multi_processing.py to train and test the model in parallel

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