Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention as a means to develop "biologically plausible" machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. In this paper, the feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied. The 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 then measured using an EEG. The CSNN's performance was compared to a standard convolutional neural network (CNN) and three graph neural networks (GNNs) via 10-fold cross-validation. The results showed that the CSNN outperformed the other neural networks.