Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

TL;DR

This study demonstrates that convolutional spiking neural networks can effectively detect anticipatory brain potentials from EEG data, outperforming other neural network models with high accuracy and robustness, including when using spike train approximations.

Contribution

The paper introduces the application of convolutional spiking neural networks for EEG-based detection of anticipatory potentials, showing superior performance over standard CNNs and neural networks.

Findings

CSNN achieved 99.06% accuracy in detecting anticipatory potentials.

CSNN outperformed CNN, EEGNet, and graph neural networks in experiments.

Performance remained robust when converting EEG data into spike trains.

Abstract

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN's performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06 percent with a true positive rate of 98.50 percent, a true negative rate of 99.20 percent and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.