Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time–Frequency Representation, and Phase Dynamics

Guang Ouyang*, Changsong Zhou*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Characterizing the brain’s dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain’s response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time–frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

Original languageEnglish
Article number1054
JournalBioengineering
Volume10
Issue number9
DOIs
Publication statusPublished - 7 Sept 2023

Scopus Subject Areas

  • Bioengineering

User-Defined Keywords

  • EEG
  • ERP
  • machine learning
  • phase dynamics
  • single trials
  • time-frequency analysis

Fingerprint

Dive into the research topics of 'Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time–Frequency Representation, and Phase Dynamics'. Together they form a unique fingerprint.

Cite this