TY - GEN
T1 - Prediction of Subsequent Memory Effects Using Convolutional Neural Network
AU - Kalafatovich, Jenifer
AU - Lee, Minji
AU - Lee, Seong Whan
N1 - 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.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Declarative memory
KW - Electroencephalogram
KW - Prediction
KW - Subsequent memory effects
UR - http://www.scopus.com/inward/record.url?scp=85092900248&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59830-3_22
DO - 10.1007/978-3-030-59830-3_22
M3 - Conference contribution
AN - SCOPUS:85092900248
SN - 9783030598297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 251
EP - 263
BT - Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings
A2 - Lu, Yue
A2 - Vincent, Nicole
A2 - Yuen, Pong Chi
A2 - Zheng, Wei-Shi
A2 - Cheriet, Farida
A2 - Suen, Ching Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020
Y2 - 19 October 2020 through 23 October 2020
ER -