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

Bibliographical note

Funding Information:
Manuscript received November 28, 2019; revised May 11, 2020 and August 20, 2020; accepted August 24, 2020. Date of publication September 10, 2020; date of current version November 6, 2020. This work was supported in part by the Institute for Information and Communications Technology Promotion (IITP) Grants funded by the Korean Government under Grant 2017-0-00451 and Grant 2019-0-00079, in part by the National Research Foundation of Korea under Grant NRF-2017M3C7A1041824, and in part by the Nazarbayev University Faculty-Development Competitive Research Grants Program under Grant 240919FD3926. (Corresponding author: Christian Wallraven.) Taeho Kang and Yiyu Chen are with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02855, South Korea.

Publisher Copyright:
© 2001-2011 IEEE.


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

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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