EEG-Based Prediction of Successful Memory Formation During Vocabulary Learning

Previous Electroencephalography (EEG) and neuroimaging studies have found differences between brain signals for subsequently remembered and forgotten items during learning of items - it has even been shown that single trial prediction of memorization success is possible with a few target items. There has been little attempt, however, in validating the findings in an application-oriented context involving longer test spans with realistic learning materials encompassing more items. Hence, the present study investigates subsequent memory prediction within the application context of foreign-vocabulary learning. We employed an off-line, EEG-based paradigm in which Korean participants without prior German language experience learned 900 German words in paired-associate form. Our results using convolutional neural networks optimized for EEG-signal analysis show that above-chance classification is possible in this context allowing us to predict during learning which of the words would be successfully remembered later.