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Decoding of human memory formation with EEG signals using convolutional networks

: Kang, T.; Chen, Y.; Fazli, S.; Wallraven, C.


6th International Conference on Brain-Computer Interface, BCI 2018 : Jan. 15 - 17, 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-2574-3
ISBN: 978-1-5386-2575-0
Art.023, 5 S.
International Winter Conference on Brain-Computer Interface (BCI) <6, 2018, Sabuk>
Fraunhofer HHI ()

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.