IEEE Access Special Section Editorial: Neural Engineering Informatics

Abstract

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, cognitive systems have been proposed as useful and effective frameworks for the modeling and understanding of brain activity patterns. They also enable direct communication pathways between the brain and external devices (brain–computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, biomedical engineers provide excellent tools to overcome the challenges of learning from brain activity patterns that are very likely to be affected by nonstationary behaviors and high uncertainty. The application of health and neural engineering to learning and modeling has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. In addition, neurobiological studies have suggested that the behavior of neural cells exhibits functional patterns that resemble the properties of computational neuroscience to encode logical perception.