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

Twoje konto

zaloguj

Wyszukiwarka

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

Abstrakt

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.

Cytowania

  • 5

    CrossRef

  • 0

    Web of Science

  • 4

    Scopus

Autorzy (14)

Cytuj jako

Pełna treść

pełna treść publikacji nie jest dostępna w portalu

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Opublikowano w:
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE strony 323 - 327,
ISSN: 0739-5175
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1109/ner.2019.8717057
Źródła finansowania:
Weryfikacja:
Politechnika Gdańska

wyświetlono 76 razy

Publikacje, które mogą cię zainteresować

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

2023

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

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

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 autorów
2021
Meta Tagi