An Ensemble CNN for Subject-Independent Classification of Motor Imagery-based EEG

Irina Dolzhikova, Berdakh Abibullaev, Reza Sameni, Amin Zollanvari

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

6 Citations (Scopus)

Abstract

Deep learning methods, and in particular Convolutional Neural Networks (CNNs), have shown breakthrough performance in a wide variety of classification applications, including electroencephalogram-based Brain Computer Interfaces (BCIs). Despite the advances in the field, BCIs are still far from the subject-independent decoding of brain activities, primarily due to substantial inter-subject variability. In this study, we examine the potential application of an ensemble CNN classifier to integrate the capabilities of CNN architectures and ensemble learning for decoding EEG signals collected in motor imagery experiments. The results prove the superiority of the proposed ensemble CNN in comparison with the average base CNN classifiers, with an improvement up to 9% in classification accuracy depending on the test subject. The results also show improvement with respect to the performance of a number of state-of-the-art methods that have been previously used for subject-independent classification in the same datasets used here (i.e., BCI Competition IV 2A and 2B datasets).

Original languageEnglish
Title of host publication43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages319-324
Number of pages6
ISBN (Electronic)9781728111797
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico
Duration: Nov 1 2021Nov 5 2021

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/TerritoryMexico
CityVirtual, Online
Period11/1/2111/5/21

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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