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.

翻译：脉冲神经网络(SNNs)因其能模拟生物系统的突触连接并产生可被近似为二进制值以提升计算效率的脉冲序列而受到日益关注。近年来，研究者引入了卷积层，将卷积网络的特征提取能力与SNNs的计算效率相结合。本文研究了使用卷积脉冲神经网络(CSNN)通过脑电图(EEG)检测人类受试者与刹车意图相关的预期慢皮质电位(SCPs)的可行性。实验数据采集自一项模拟城市环境的测试平台，受试者操控遥控车辆，并通过音频倒计时提醒即将到来的刹车事件以诱发预期电位，该电位通过EEG测量。通过10折交叉验证，将CSNN的性能与标准CNN、EEGNet及三种图神经网络进行比较。CSNN表现优于所有其他神经网络，预测准确率达99.06%，真阳性率为98.50%，真阴性率为99.20%，F1分数为0.98。在使用定位SCPs的EEG通道子集进行的消融研究中，CSNN性能与CNN相当。通过增量调制将浮点型EEG数据转换为脉冲序列以模拟突触连接时，CSNN的分类性能仅轻微下降。