Decoding of human memory formation with EEG signals using convolutional networks

Taeho Kang, Yiyu Chen, Siamac Fazli, Christian Wallraven

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This study examines whether it is possible to predict successful memorization of previously-learned words in a language learning context from brain activity alone. Participants are tasked with memorizing German-Korean word association pairs, and their retention performance is tested on the day of and the day after learning. To investigate whether brain activity recorded via multi-channel EEG is predictive of memory formation, we perform statistical analysis followed by single-trial classification: First by using linear discriminant analysis, and then with convolutional neural networks. Our preliminary results confirm previous neurophysiological findings, that above-chance prediction of vocabulary memory formation is possible in both LDA and deep neural networks.

Original languageEnglish
Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2018-January
ISBN (Electronic)9781538625743
DOIs
Publication statusPublished - 2018 Mar 9
Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
Duration: 2018 Jan 152018 Jan 17

Other

Other6th International Conference on Brain-Computer Interface, BCI 2018
CountryKorea, Republic of
CityGangWon
Period18/1/1518/1/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience

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  • Cite this

    Kang, T., Chen, Y., Fazli, S., & Wallraven, C. (2018). Decoding of human memory formation with EEG signals using convolutional networks. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311487