Decoding of human memory formation with EEG signals using convolutional networks

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.