  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.
