TY - GEN
T1 - Decoding of human memory formation with EEG signals using convolutional networks
AU - Kang, Taeho
AU - Chen, Yiyu
AU - Fazli, Siamac
AU - Wallraven, Christian
N1 - 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.
PY - 2018/3/9
Y1 - 2018/3/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85050809251&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2018.8311487
DO - 10.1109/IWW-BCI.2018.8311487
M3 - Conference contribution
AN - SCOPUS:85050809251
T3 - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
SP - 1
EP - 5
BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Brain-Computer Interface, BCI 2018
Y2 - 15 January 2018 through 17 January 2018
ER -