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

Krishnakant V. Saboo, Yogatheesan Varatharajah, Brent M. Berry, Michael R. Sperling, Richard Gorniak, Kathryn A. Davis, Barbara C. Jobst, Robert E. Gross, Bradley Lega, Sameer A. Sheth, Michael J. Kahana, Michal T. Kucewicz, Gregory A. Worrell, Ravishankar K. Iyer

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

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.

Original languageEnglish (US)
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages323-327
Number of pages5
ISBN (Electronic)9781538679210
DOIs
StatePublished - May 16 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period3/20/193/23/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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    Saboo, K. V., Varatharajah, Y., Berry, B. M., Sperling, M. R., Gorniak, R., Davis, K. A., Jobst, B. C., Gross, R. E., Lega, B., Sheth, S. A., Kahana, M. J., Kucewicz, M. T., Worrell, G. A., & Iyer, R. K. (2019). A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 323-327). [8717057] (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717057