Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces

Berdakh Abibullaev, Amin Zollanvari

Research output: Contribution to journalArticle

Abstract

Constructing accurate predictive models is at the heart of brain-computer interfaces (BCIs) because these models can ultimately translate brain activities into communication and control commands. The majority of the previous work in BCI use spatial, temporal, or spatiotemporal features of event-related potentials (ERPs). In this study, we examined the discriminatory effect of their spatiospectral features to capture the most relevant set of neural activities from electroencephalographic recordings that represent users' mental intent. In this regard, we model ERP waveforms using a sum of sinusoids with unknown amplitudes, frequencies, and phases. The effect of this signal modeling step is to represent high-dimensional ERP waveforms in a substantially lower dimensionality space, which includes their dominant power spectral contents. We found that the most discriminative frequencies for accurate decoding of visual attention modulated ERPs lie in a spectral range less than 6.4 Hz. This was empirically verified by treating dominant frequency contents of ERP waveforms as feature vectors in the state-of-the-art machine learning techniques used herein. The constructed predictive models achieved remarkable performance, which for some subjects was as high as 94% as measured by the area under curve. Using these spectral contents, we further studied the discriminatory effect of each channel and proposed an efficient strategy to choose subject-specific subsets of channels that generally led to classifiers with comparable performance.

Original languageEnglish
Article number8613780
Pages (from-to)2009-2020
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number5
DOIs
Publication statusPublished - Sep 1 2019

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Evoked Potentials
Learning
Decoding
Learning systems
Brain
Classifiers
Area Under Curve
Communication

Keywords

  • Brain-computer interfaces
  • EEG
  • ERPs
  • machine learning
  • P300
  • signal processing

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Cite this

Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces. / Abibullaev, Berdakh; Zollanvari, Amin.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 23, No. 5, 8613780, 01.09.2019, p. 2009-2020.

Research output: Contribution to journalArticle

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