Course project for EI328 Science and Technology Innovation 4J (Parallel Machine Learning with Application to Large-Scale Data Mining), tutored by Prof.Bao-Liang Lu.
Propose a domain generalization solution via feature manipulation to personalize EEG-based emotion classification.
A subset of SJTU Emotion EEG Dataset (SEED) authored by BCMI lab led by Prof.Bao-Liang Lu.
Data can be downloaded from this link. Then put EEG_X.mat and EEG_Y.mat into ./data folder.
It contains 15 human subjects in 3394 time steps. 310-dimensional differential entropy EEG-feature is collected for each human at each time step. Each 310-dimensional EEG-feature is annotated with a emotion label, including 3 categories (0 for clam, -1 for sad, 1 for happy).
First do the unsupervised pretraining of IDN by command
python run_IDN.py --lambda_rec 1e-3 --lambda_dom 1e-2 --lambda_cross 1e-2 --lambda_mmd 1 --epoch 100
Then the weight after first training stage will be stored in weights/run_IDN.pth
Second, we are going to do classification with LSTM based on pre-trained weight.
python run_LSTM.py --lambda_cls 0.01 --epoch 300 --IDN_weight weights/run_IDN.pth
Then the final weight will be stored in weights/run_LSTM.pth
python test_model.py --IDN_LSTM_weight weights/run_LSTM.pth
It will restore the weight in weights/run_LSTM.pth and make evaluation.