Predicting the Transition from Short-term to Long-term Memory based on Deep Neural Network
This study addresses the problem of predicting long-term memory from short-term memory EEG signals, which could potentially improve learning efficiency and assist individuals with memory impairments, but the reported kappa values are low.
This paper investigates the prediction of long-term memory formation using EEG signals recorded during successful short-term memory. They used MLP and CNN classifiers on spectral power features, achieving a kappa-value of 0.19 for picture memory with CNN and 0.32 for location memory with MLP.
Memory is an essential element in people's daily life based on experience. So far, many studies have analyzed electroencephalogram (EEG) signals at encoding to predict later remembered items, but few studies have predicted long-term memory only with EEG signals of successful short-term memory. Therefore, we aim to predict long-term memory using deep neural networks. In specific, the spectral power of the EEG signals of remembered items in short-term memory was calculated and inputted to the multilayer perceptron (MLP) and convolutional neural network (CNN) classifiers to predict long-term memory. Seventeen participants performed visuo-spatial memory task consisting of picture and location memory in the order of encoding, immediate retrieval (short-term memory), and delayed retrieval (long-term memory). We applied leave-one-subject-out cross-validation to evaluate the predictive models. As a result, the picture memory showed the highest kappa-value of 0.19 on CNN, and location memory showed the highest kappa-value of 0.32 in MLP. These results showed that long-term memory can be predicted with measured EEG signals during short-term memory, which improves learning efficiency and helps people with memory and cognitive impairments.