Predicting Performance in Working Memory During the Waking Period by Applying a Convolutional Neural Network to EEG Data in the N-Back Task: A Pilot Study.

Memory performance is regulated by circadian rhythms, and electroencephalograms (EEG) measure biological signals related to memory mechanisms and circadian rhythms. Therefore, EEG could be used to detect changes in diurnal memory. In this study, we measured the EEG signals of participants conducting a memory-related task and tested the effectiveness of a convolutional neural network (CNN) in predicting memory task performance at different times. EEG signals from participants performing N-back tasks at 8-9 a.m. and 3-4 p.m. were recorded. While performance showed no significant differences between times, differences were observed in EEG relative power. A CNN was trained using the relative power and raw waveform data of the EEG signals recorded during the tasks. When predicting the time at which the working memory (WM) was enhanced, the relative power CNN exhibited a significantly higher accuracy than the raw waveform CNN. However, the performance dropped in the test where the training data did not include the EEG data of the same participant. Overall, these results suggest that while EEG signals using a relative power CNN have high predictive potential, developing a personalized classification system that reflects individual chronotypes is effective for practical applications.