Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures | Frontiers Research Topic

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Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks. 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, 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, for usability evaluation and for tutoring applications. While there exists a huge body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal with 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 EEG, fNIRS or physiological signals such as EDA, ECG, BVP or Eyetracking 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.In this research topic, we are looking for: (1) studies in complex, realistic scenarios that specifically deal wit

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