Editorial: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures

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 Gerjets et al., 2014, or by shutting down distractions from the mobile phone). Single-trial classifiers based on brain activity measurements such as electroencephalography (EEG, Kothe and Makeig, 2011; Lotte et al., 2018), functional near-infrared spectroscopy (fNIRS, Putze et al., 2014; Herff et al., 2015), physiological signals (Fairclough et al., 2005; Fairclough, 2008), or eye tracking (Putze et al., 2013) have the potential to classify affective (Koelstra et al., 2010; Heger et al., 2014; Mühl et al., 2014) or cognitive states based upon short segments of data. Two of them use fNIRS as a different mode to capture neural activity (two others use fNIRS as single modality); two others use eye tracking as a way to capture visual attention and one also uses physiological signals (such as heart rate and breath rate). [...]they found no evidence of typical neural correlates of affect.