- Python==3.6.13
- pytorch==1.8.1
- matplotlib==3.2.2
- numpy==1.19.5
- h5py==2.10.0
- scikit-learn==0.24.2
- scipy==1.5.3
- Data Segmentation & Feature Extraction: In the filefolder "1. Data Segmentation & Feature Extraction", excute the file "dataSegmentationAndFeatureExtraction.m" to segment the data and extract the features, and obtain the samples to be classified. The ready-to-classify samples at different levels (5s-sample and individual) were already stored in the file "Segmented Data".
- Baseline Model Training: In the filefolder "2. Baseline Model Training & Direct Transferring Prediction & Fine Tuning\*** Level", where "***" indicates different levels, execute the file "cnnClassification_***-PolandTrained.py" to train the baseline models.
- Direct Transferring Prediction: In the filefolder "2. Baseline Model Training & Direct Transferring Prediction & Fine Tuning\*** Level", where "***" indicates different levels, execute the file "cnnClassification_***-RussiaTested.py" to obtain the baseline transferring accuracy.
- Fine Tuning the Trained Models: In the filefolder "2. Baseline Model Training & Direct Transferring Prediction & Fine Tuning\*** Level", where "***" indicates different levels, execute the file "cnnClassification_individual-RussiaTested-FineTuning.py" to obtain the fine-tuning transferring accuracy.
- Applying Transfer Component Analysis (TCA): TCA is implemented in the file "3. TCA & TCA_FT\cnnClassification_Individual-TCA-TCA_FT.py".
- Train the Models on the Transformed Samples and Fine Tuning the Trained Models: Execute the file "3. TCA & TCA_FT\cnnClassification_Individual-TCA-TCA_FT.py" to obtain the direct transferring predictions of model trained on the TCA-transformed samples, and the corresponding fine-tuning transferring accuracy.
- Figure Plotting: Execute the file "4. Figure Plotting\resultAnalysis.m". The plotted results were already in the filefolder "4. Figure Plotting".
More details please refer to the file "EEG精神分裂症分类任务的迁移学习.pdf" (Requested from [email protected], in mandarin btw).
- Singh, K., S. Singh, and J. Malhotra, Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H, 2021. 235(2): p. 167-184.
- Pan, S.J., et al., Domain Adaptation via Transfer Component Analysis. IEEE Transactions on Neural Networks, 2011. 22(2): p. 199-210.