• Propose a self-supervised framework for EEG-based object recognition with contrastive learning, achieving remarkable zero-shot performance on large and rich datasets.
• Demonstrate the feasibility of investigating image information from EEG signals, by resolving brain activity from temporal, spatial, spectral, and semantic aspects.
• Apply two plug-and-play modules to capture spatial correlations among EEG channels, offering evidence that the model discerns the spatial dynamics of object recognition.

many thanks for sharing good datasets!

1. Things-EEG2
2. Things-MEG (updating)

• ./preprocessing/

• raw data: ./Data/Things-EEG2/Raw_data/
• proprocessed eeg data: ./Data/Things-EEG2/Preprocessed_data_250Hz/

1. pre-processing EEG data of each subject

   • modify preprocessing_utils.py as you need.
     • choose channels
     • epoching
     • baseline correction
     • resample to 250 Hz
     • sort by condition
     • Multivariate Noise Normalization (z-socre is also ok)
   • python preprocessing.py for each subject.

2. get the center images of each test condition (for testing, contrast with EEG features)

   • get images from original Things dataset but discard the images used in EEG test sessions.

Now we release the image features extracted with CLIP model in ./dnn_feature/.

• ./nice_stand.py

• ./visualization/

Hope this code is helpful. I would appreciate you citing us in your paper. 😊

@misc{song2023decoding,
  title = {Decoding {{Natural Images}} from {{EEG}} for {{Object Recognition}}},
  author = {Song, Yonghao and Liu, Bingchuan and Li, Xiang and Shi, Nanlin and Wang, Yijun and Gao, Xiaorong},
  year = {2023},
  month = nov,
  number = {arXiv:2308.13234},
  eprint = {2308.13234},
  primaryclass = {cs, eess, q-bio},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.2308.13234},
  archiveprefix = {arxiv}
}