The task requires us to classify electroencephalography(EEG) into six categories, including human body, human face, animal body, animal face, fruit vegetable and inanimate object. The EEG dataset was acquired from the brain of participants responding to different photography. These photographs that participants are required to watch can be classified into six aforementioned categories.
  To achieve the classification task, in this project, I reference the model structure of STGCN[1] and get the 42% prediction precision of classification to six categories. I construct an adjacent matrix through node embedding technology and then use the GCN method to obtain spatial features of EEG.

Configurations: related configuration about EEG clissification model
Data: It is a folder, which used to save raw dataset and processed dataset.
Data process model：eeg_process.py, read EEG dataset，and split dataset into train, val and test set.
Data upload model：eeg_dataset.py 
Model：eeg_network.py
Train：eeg_train.py

The downlaod addresse of EEG datasets:
https://exhibits.stanford.edu/data/catalog/tc919dd5388

Run data process model to split data into train,val and test set.

python data_process --config_path ./configurations/eeg_config.yaml

Train EEG classification model

python eeg_train.py --config_path ./configurations/eeg_config.yaml

[1] Kaneshiro, Blair, et al. "A representational similarity analysis of the dynamics of object processing using single-trial EEG classification." Plos one 10.8 (2015): e0135697.
[2] Yu, Bing, Haoteng Yin, and Zhanxing Zhu. "Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting." arXiv preprint arXiv:1709.04875 (2017).