Deep Learning Models for Subject-Independent ERP-based Brain-Computer Interfaces

Adilet Tuleuov, Berdakh Abibullaev

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

Abstract

Inter-subject variabilities in brain signals can significantly deteriorate the accuracy of brain-computer interface (BCI) systems. Every subject has different brain signals; also, the performance of a subject varies widely between sessions, within a session, between on-line and off-line settings, and even from epoch to epoch. Such variabilities arise in measurements obtained during different BCI sessions require subject and session specific decoder models. This work proposes three different deep learning models for subject-independent decoding of event-related potentials in electroencephalographic signals: 1) shallow convolutional neural network, 2) gated recurrent neural network, and 3) CNN-RNN-Net: a hybrid one-dimensional convolution and a gated recurrent unit model. Experimental results demonstrate that the proposed models outperform the conventional baseline models in decoding subject-independent data. Moreover, among the three models, the CNN-RNN-Net has shown improved classification results.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages945-948
Number of pages4
ISBN (Electronic)9781538679210
DOIs
Publication statusPublished - 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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