Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks, such as problem solving, navigation, or planning. Estimation of constructs like working memory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g. by simplifying the exercises of a tutor system when the subject is overloaded, or by shutting down distractions from the mobile phone). The determination of cognitive states like working memory load is also useful for automated testing/assessment or for usability evaluation. While there exists a large body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal witt the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as electroencephalography, functional near-infrared spectroscopy, physiological signals or eye tracking have the potential to classify affective or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces. The goal of this Frontiers Research Topic was to advance the State-of-the-Art in signal-based modeling of cognitive processes. We were especially interested in research towards more complex and realistic study designs, for example collecting data in the wild or investigating the interaction between different cognitive processes or signal modalities. Bringing together many contributions in one format allowed us to look at the state of convergence or diversity regarding concepts, methods, and paradigms.