Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface

Mikito Ogino, Suguru Kanoga, Shin Ichi Ito, Yasue Mitsukura

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets - AMUSE and PASS2D - using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

Original languageEnglish
Article number9381874
Pages (from-to)47008-47023
Number of pages16
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Auditory stimuli
  • P300
  • brain-computer interface
  • event-related potential
  • semi-supervised learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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