AUTHOR=Friedman Nir , Fekete Tomer , Gal Kobi , Shriki Oren TITLE=EEG-Based Prediction of Cognitive Load in Intelligence Tests JOURNAL=Frontiers in Human Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2019.00191 DOI=10.3389/fnhum.2019.00191 ISSN=1662-5161 ABSTRACT=

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well.