A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

Abstract

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

An efficient intelligent system for the classification of electroencephalography (EEG) brain signals using nuclear features for human cognitive tasks

“…Finally, the discriminative features were input to the simple and efficient CMMDC to predict whether a subject belongs to HA or LA group in raw as well as clean data, classify the correct and incorrect answers, and classify EO and EC brain states. The detail of the experimental material, subjects, data collection procedure and processing EEG signals after recording is discussed in [33,34].…”

“…In eyes open / eyes closed (EOEC) dataset, thirty subjects (34) subjects were participated in the baseline task, i.e. EO and EC.…”

“…To investigate the diseases effects on memory recall ability and fluid intelligence level, some subjects will also be considered in future study that might have some medical problems. In this study, there were only thirty-four (34) subjects; therefore, number of subjects should be increased to validate that EEG brain signals would be enough to predict the cognitive performance. Moreover, this research study examined the association of EEG brain signals with learning ability and memory for young subjects only.…”

On the joint use of Artificial Intelligence and Brain-Imaging Techniques in Technology-enhanced Learning Environments: A Systematic Literature Review

“…The CNN and LSTM neural network's results were much better than the MLP's results, achieving an accuracy of 94.16% compared to the MLP's 82.50% accuracy, which, as the authors say, "clearly demonstrated that temporal information is important for signal analysis". Bamatraf et al (2016) chose SVM with RBF kernel to classify (predict) true and false memories, both short term memories (STM) and long term memories (LTM). The data was collected by an EEG device while the learners watched learning material; half the learners watched in 2D, and the other half watched the same material in 3D.…”

Cognitive and neural mechanisms underlying false memories: misinformation, distortion or erroneous configuration?

“…As such the pivotal question, as to whether brain activity during the initial memory encoding and consolidation or long-term retrieval differs in relation to whether items are subsequently remembered or missed, is a possibility to be explored [4] . Neuroimaging, in particular functional magnetic resonance imaging (fMRI) [5] and neurophysiological measures such as intracranial electroencephalogram (iEEG) [6] , has allowed researchers to extend behavioral research and establish a relationship between generalized patterns of brain activity and cognitive processes that support memory formation and retrieval [7] – [9] .…”