Data Augmentation for P300-based Brain-Computer Interfaces Using Generative Adversarial Networks

Kassymzhomart Kunanbayev, Berdakh Abibullaev, Amin Zollanvari

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

7 Citations (Scopus)

Abstract

A notable problem in Brain-Computer Interface (BCI) is the burden of collecting an adequate amount of training data to be used in estimating a robust classifier model. This study explores the prospect of using Generative Adversarial Networks (GANs), a novel family of generative models, to perform data augmentation for generating artificial training data that are used in the classification of P300 event-related potentials in electroencephalogram (EEG) signals. In this regard, we consider two popular GANs, namely, Deep Convolutional Generative Adversarial Networks (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), and explore their efficacy in generating artificial EEG data in both subject-specific and subject-independent contexts. The results show that data augmentation using both DCGAN and WGAN-GP could lead to improved classification performance. However, the operating conditions that an improvement is observed differ between the subject-specific and subject-independent classification schemes. In particular, we observe that a transition from a relatively small to large training sample size per subject would generally lead to a better classification performance in training subject-specific classifiers; however, when a limited number of subjects is available, this transition could potentially result in an opposite effect in case of subject-independent classification.

Original languageEnglish
Title of host publication9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728184852
DOIs
Publication statusPublished - Feb 22 2021
Event9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of
Duration: Feb 22 2021Feb 24 2021

Publication series

Name9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021

Conference

Conference9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Country/TerritoryKorea, Republic of
CityGangwon
Period2/22/212/24/21

Keywords

  • data augmentation
  • generative adversatial networks
  • P300
  • subject-independent
  • subject-specific
  • Deep Learning
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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