A repository including multiple attentional module designs for brain signal recognition tasks. Scripts should run with tensorflow>=2.8.0
Preprints associated with this repo:
* KAM -- a Kernel Attention Module for Emotion Classification with EEG Data
* A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data

cv_exp.sh: Bash script for calling exp_5CV_SEED.py with data from different subjects in SEED dataset.
DEAP_benchmark.sh: Bash script for calling DEAP_test_gen.py specifying different subjects, experiment types and models with DEAP dataset.
exp_5CV_SEED.py: Main script for training with SEED dataset. One should manually change models used in the script.
DEAP_test_gen.py: Main script for training with DEAP dataset.

These are less organised scripts. They were written as our experiments goes. So you probably will not use all contents in a given script, but just some parts of it for your purpose.

DEAP_Plots.py: Gather metrics from trained models with DEAP for benchmarking, analysis and make some visualizations.
SEED_extra_plot.py: Gather metrics from trained models with SEED for benchmarking, analysis and make some visualizations.
sigma_effect.py: Scripts for exploring the Kernel Attention Module (KAM)'s effect on backbone network with SEED dataset.

The folder scalp_map contains matlab files for making scalp maps.
Modules.py: Contains custom layers used for constructing network models.
Models.py: Contains different network models. Utils.py: Some utility functions Visual.py: Some function for general visualization purpose.

Figures below are:

• A simple demo of the idea of prediction transition curve on trinary classification tasks.
• The prediction transition curve under different models given the same input samples from SEED dataset.
• Track of accuracies when different frequencies are filtered out from input samples.
• The map from deep gram matrix's element to attention matrix's element learned by MCAM module
• Different scalp patterns (kernel weights of the first Depthwise convolutional layer) when various attention modules are inserted. First row: mean, second row: std, third row: top-5 correlated channel pairs (red: +, blue: -).
• The effect of different $\alpha$ value on layer dense layer's output. Visualized via UMAP.