Prediction of Subsequent Memory Effects Using Convolutional Neural Network

Jenifer Kalafatovich, Minji Lee, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Differences in brain activity have been associated with behavioral performance in memory tasks; in order to understand memory processes, previous studies have explored them and attempted to predict when items are later remembered or forgotten. However, reported prediction accuracies are low. The aim of this research is to predict subsequent memory effects using a convolutional neural network. We additionally compare different methods of feature extraction to understand relevant information related to memory processes during pre and on-going stimulus intervals. Subjects performed a declarative memory task while electroencephalogram signals were recorded from their scalp. The signals were epoched regarding stimulus onset into pre and on-going stimulus and used for prediction evaluation. A high prediction accuracy was obtained when using convolutional neural networks (pre-stimulus: 71.64% and on-going stimulus: 70.50%). This finding showed that it is possible to predict successful remember items on a memory task using a convolutional neural network, with a higher accuracy than conventional methods.

Original languageEnglish
Title of host publicationPattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings
EditorsYue Lu, Nicole Vincent, Pong Chi Yuen, Wei-Shi Zheng, Farida Cheriet, Ching Y. Suen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030598297
Publication statusPublished - 2020
Event2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020 - Zhongshan, China
Duration: 2020 Oct 192020 Oct 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12068 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

Bibliographical note

Funding Information:
Acknowledgements. This work was partly supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Department of Artificial Intelligence (Korea University)).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Convolutional neural network
  • Declarative memory
  • Electroencephalogram
  • Prediction
  • Subsequent memory effects

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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