Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG.