A system based on 3D and 2D educational contents for true and false memory prediction using EEG signals

Electroencephalography (EEG) has been widely adopted for investigating brain behavior in different cognitive tasks e.g. learning and memory. In this paper, we propose a pattern recognition system for discriminating the true and false memories in case of short-term memory (STM) for 3D and 2D educational contents by analyzing EEG signals. The EEG signals are converted to scalp-maps (topomaps) and city-block distance is applied to reduce the redundancy and select the most discriminative topomaps. Finally, statistical features are extracted from selected topomaps and passed to Support Vector Machine (SVM) to predict brain states corresponding to true and false memories. A sample of thirty four healthy subjects participated in the experiments, which consist of two tasks: learning and memory recall. In the learning task, half of the participants watched 2D educational contents and half of them watched the same contents in 3D mode. After 30 minutes of retention, they were asked to perform memory recall task, in which EEG signals were recorded. The classification accuracy of 97.5% was achieved for 3D as compared to 96.5% for 2D. The statistical analysis of the results suggest that there is no significant difference between 2D and 3D educational contents on STM in terms of true and false memory assessment.