  Many studies have explored brain signals during the performance of a memory
task to predict later remembered items. However, prediction methods are still
poorly used in real life and are not practical due to the use of
electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently
used to measure brain signals due to its flexibility when applying it to real
world environments. In this study, we attempt to predict whether a shown
stimulus is going to be remembered or forgotten using ear-EEG and compared its
performance with scalp-EEG. Our results showed that there was no significant
difference between ear-EEG and scalp-EEG. In addition, the higher prediction
accuracy was obtained using a convolutional neural network (pre-stimulus:
74.06%, on-going stimulus: 69.53%) and it was compared to other baseline
methods. These results showed that it is possible to predict performance of a
memory task using ear-EEG signals and it could be used for predicting memory
retrieval in a practical brain-computer interface.
