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
    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

    Publication series

    Name2018 6th International Conference on Brain-Computer Interface, BCI 2018
    Volume2018-January

    Other

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

    Bibliographical note

    Funding Information:
    ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451). This publication only reflects the authors views. Funding agencies are not liable for any use that may be made of the information contained herein.

    Publisher Copyright:
    © 2018 IEEE.

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Behavioral Neuroscience

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