LightBunny: Lightweight Neural Network for EEG Source Imaging

LightBunny is an EEG source imaging network aiming at providing an estimation of the location, size, and temporal activity of the brain activities from scalp EEG/MEG recordings.

[KOSOMBE 2024 Spring] Suyoung Hwang, Jinseok Eo, Wooyong Lee, Hae-Jeong Park: "Electrophysical Source Imaging based on the Spatiotemporal Neural Network"

This work is supported by the National Research Foundation (NRF) funded by the Korean government. (No. 2022MSE5E8018285)

Overview of Network Architecture and Results

Network Architecture

• Default Training Setup: MMSE loss, Adam optimizer with learning rate 5e-4
• Default Hyperparameters: $C = 64$, $T = 500$, $D = 256$, $R = 994$, $S = 25$, $F = D / 4$
  
• Total Parameter Count: 5.06M

Results

• Spatial PCC (time-averaged region activity): 0.85 ± 0.05
  
• Temporal PCC (source region): 0.98 ± 0.03

(A, B) Ground Truth (A) and Estimated (B) Source of a Generated Sample Test Data
(C) Ground Truth and Estimated Potential of a Source Region of a Generated Sample Test Data
(D) Estimated Source of the Auditory-Evoked Potential Elicited in the Auditory Oddball Task

Requirements

All python requirements can be installed with Poetry.

To set up the environment:

poetry install

To activate the environment:

poetry shell

Also requires MATLAB with appropriate toolbox installed.

Train Data Generation

TVB Simulation and Postprocessing

mkdir source
cd forward
./simulate.sh 0 994

forward/simulate.sh runs the simulation for regions within provided region range; divide the regions into groups of 16 and perform them in parallel.
Since raw NMM simulation results are very large; the script post-processes the results at the end of each group's simulation.
During post-processing, the script automatically runs MATLAB in batch mode.

Prepare Training/Testing Dataset

matlab -batch generate_sythetic_source

After running the MATLAB script, follow instructions in prep_metadata.ipynb.

To switch between training/testing dataset, modify train flag in forward/generate_sythetic_source.m and corresponding filenames in prep_metadata.ipynb

Forward Calculation

Follow instructions in prep_leadfield.ipynb.

By default, the script calculates the forward solution to 64-channel EEF on a standard 10-20 system based on the fsaverage template, but this can be modified to other templates or other systems.

Training & Evaluation

Follow instructions in main.ipynb.

Evaluation with real data (.edf, etc.) requires realigning that data to the channel orientation of the forward solution used to train the network. The scripts in prep_edf.ipynb can help with this task.