Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics. Our brains are buzzing with electrical activity moving in and out of neural cells, sending electrical impulses along their axons, and exchanging chemical messages. Neural signals allow us to observe neuronal activity in real time. This special issue aims to cover some problems related to neural signal processing. The origin of this volume is in the Special Sessions on Challenges in Neuroengineering (SSCN) within the International Conference on Neural Computation Theory and Applications (NCTA). A selected choice of papers based on the presentations delivered at SSCN has given rise to this issue of Cognitive Computation. The papers hereinafter deal with the following topics: