Joint maximum likelihood time delay estimation of unknown event-related potential signals for EEG sensor signal quality enhancement

Kyungsoo Kim, Sung Ho Lim, Jaeseok Lee, Ji Woong Choi, Won Seok Kang, Cheil Moon

Research output: Contribution to journalArticle

Abstract

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.

Original languageEnglish (US)
Article number891
JournalSensors
Volume16
Issue number6
DOIs
StatePublished - Jun 16 2016
Externally publishedYes

Keywords

  • EEG
  • ERP
  • Maximum likelihood (ML)
  • Synchronization
  • Time delay estimation (TDE)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Analytical Chemistry
  • Biochemistry

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