Source code for the Explainable Consciousness Indicator (ECI)

This project contains the scripts associated to the manuscript "Quantifying Arousal and Awareness in Altered States of Consciousness using Interpretable Deep Learning."

• Programming Language: MATLAB
• Contact: Minji Lee (minjilee@korea.ac.kr)

Step 1: EEG preprocessing and converted data

The raw EEG signals were converted into spatiotemporal 3D matrix.

Step 2: Training CNN

The converted 3D feature was used on a convolutional neural network (CNN) in the two components of consciousness: arousal and awareness. In each arousal and awareness state, the EEG data were trained as two classes (low versus high). For training and test phase, we used the leave-one subject-out approach as transfer learning.

Step 3: Testing CNN and Calculating ECI

The output indicates the probability was averaged for calculating ECI. Finally, relevance scores based on layer-wise relevance propagation (LRP) was calculated.

Etc

Scatter plot & Topo plot & Violin plot

The EEGLAB toolbox is freely available at https://sccn.ucsd.edu/eeglab/download.php. Source code for CNN and LRP is freely available online at https://github.com/sebastian-lapuschkin/lrp_toolbox. Source code for violin plot is available from https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot. Source code for shaded error bar is available from https://github.com/raacampbell/shadedErrorBar.