This is a final project for my 2019 Winter course "Pattern Recognition" based on DEAP dataset. We preprocess original signal data via CWT(Continuous Wavelet Transform) and bulit a 3D-CNN architecture as classifier, the accuracy of 'valence' label reaches 84.34%.

1. CWT analysis
   Baseline is removed, and original siginal data tramsformed into wavelet coefficients through CWT, then further into wavelet energy (scalograms). In this step, we transform data shape from 32(channel)*8064(sample points) into 32*64(scale)*7680(sample points).

2. Cut frames
   Next, we set 1s as frame length, thus 60 frames can be got within a 60s video. The shape for each frame is 32(channel)*64(scale).

3. Select scales
   Then we calculated mean EER for all 64 scales in 32 channels. And 8th~39th scales are selected to reduce caculation.

4. 3D chunk
   We select several continous frames and stack them togther as a 3D chunk. The later experiments proved that 3 is the best.

The network architecture is as follows.

We use matlab_preprocessed_data, which is excluded from this repo.
Run 'cwt_process.m' to get 'File_60frame_exscale'.
Run 'scale_select.m' to get 'sumEER.mat' & 'scale_select.png'. Run '3d_cnn.ipynb' based on files in 'File_60frame_exscale'.
Besides, part of data are used to train 3D-CNN, some parameters of it are stored in 'model_statedict.pth' & 'optimizer_statedict.pth'. Therefore, 'demo.py' can directly do the predict task based on the 'x_test.pth' & 'y_test.pth'.

• Li, X., et al., Emotion Recognition from Multi-Channel EEG Data through Convolutional Recurrent Neural Network, in 2016 Ieee International Conference on Bioinformatics and Biomedicine, T. Tian, et al., Editors. 2016. p. 352-359.
• Salama, E.S., et al., EEG-Based Emotion Recognition using 3D Convolutional Neural Networks. International Journal of Advanced Computer Science and Applications, 2018. 9(8): p. 329-337.
• DEAP Dataset: https://www.eecs.qmul.ac.uk/mmv/datasets/deap/