A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection - Publication - Bridge of Knowledge

Your account

login

Search

A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection

Abstract

Computational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic regression prediction model was built using spectral features of intracranial electroencephalography (iEEG) from the selected electrodes. We demonstrate our method on iEEG data from 37 human subjects performing free recall verbal short-term memory tasks. The method achieves a 36.3% reduction in the number of electrodes used for prediction, resulting in a 64.9% reduction in inference computation time with just a 0.3% loss in prediction performance compared to the case when all electrodes were used. The electrodes selected using our method provided improved prediction performance compared to those electrodes that were not selected in 31 out of 37 patients. Building upon this observation, we also developed a method to identify the subjects for whom the proposed electrode selection method would be beneficial.

Citations

  • 6

    CrossRef

  • 0

    Web of Science

  • 4

    Scopus

Authors (14)

Cite as

Full text

full text is not available in portal

Keywords

Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Published in:
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE pages 323 - 327,
ISSN: 0739-5175
Language:
English
Publication year:
2019
Bibliographic description:
Saboo K., Varatharajah Y., Berry B., Sperling M., Gorniak R., Davis K., Jobst B., Gross R., Lega B., Sheth S., Kahana M., Kucewicz M. T., Worrell G., Iyer R.: A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection// / : , 2019, s.323-327
DOI:
Digital Object Identifier (open in new tab) 10.1109/ner.2019.8717057
Sources of funding:
Verified by:
Gdańsk University of Technology

seen 119 times

Recommended for you

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

  • K. Saboo,
  • Y. Varatharajah,
  • B. M. Berry
  • + 10 authors
2019

Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation

2023

Combined Single Neuron Unit Activity and Local Field Potential Oscillations in a Human Visual Recognition Memory Task

2016

Introduction to the special issue on machine learning in acoustics

  • Z. Michalopoulou,
  • P. Gerstoft,
  • B. Kostek
  • + 1 authors
2021
Meta Tags