Real-time Detection of Evoked Potentials by Deep Learning: a Case Study

In Local Field Potential (LFP) recordings it is hard to distinguish Evoked Potentials (EPs) from spontaneous activity. Automatic real-time detection of all EPs in a recording would enable the deployment of neuromorphic prostheses. In this paper, we present a case study involving EPs induced by stimulation of a whisker in rats. We compare the detection performance of three deep learning models: a Temporal Convolutional Network, a Recurrent Neural Network, and a Mixed model. A data augmentation technique for LFP data and a technique to learn the delay of causal models are proposed. Experimental results show that the three deep learning models are capable of detecting most EPs with few false positives, a delay of less than 100ms, and for a pruned TCN, using only 1,282 parameters.