EEG-based prediction of cognitive load in intelligence tests

Nir Friedman, Tomer Fekete, Kobi Gal, Oren Shriki

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


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.

Original languageEnglish
Article number191
JournalFrontiers in Human Neuroscience
StatePublished - 1 Feb 2019


  • Brain-computer interface
  • Cognitive load
  • Electroencephalography
  • Machine learning
  • Raven's matrices

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience


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