EEG-Based prediction of successful memory formation during vocabulary learning

Taeho Kang, Yiyu Chen, Siamac Fazli, Christian Wallraven

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Previous Electroencephalography (EEG) and neuroimaging studies have found differences between brain signals for subsequently remembered and forgotten items during learning of items - it has even been shown that single trial prediction of memorization success is possible with a few target items. There has been little attempt, however, in validating the findings in an application-oriented context involving longer test spans with realistic learning materials encompassing more items. Hence, the present study investigates subsequent memory prediction within the application context of foreign-vocabulary learning. We employed an off-line, EEG-based paradigm in which Korean participants without prior German language experience learned 900 German words in paired-associate form. Our results using convolutional neural networks optimized for EEG-signal analysis show that above-chance classification is possible in this context allowing us to predict during learning which of the words would be successfully remembered later.

Original languageEnglish
Article number9193957
Pages (from-to)2377-2389
Number of pages13
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number11
Publication statusPublished - 2020 Nov


  • BCI
  • Electroencephalography (EEG)
  • learning
  • subsequent memory prediction

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation


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