Positive classification advantage: Tracing the time course based on brain oscillation

Tianyi Yan*, Xiaonan Dong, Nan Mu, Tiantian Liu, Duanduan Chen, Li Deng, Changming Wang, Lun Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

The present study aimed to explore the modulation of frequency bands (alpha, beta, theta) underlying the positive facial expressions classification advantage within different post-stimulus time intervals (100–200 ms, 200–300 ms, 300–400 ms). For this purpose, we recorded electroencephalogram (EEG) activity during an emotion discrimination task for happy, sad and neutral faces. The correlation between the non-phase-locked power of frequency bands and reaction times (RTs) was assessed. The results revealed that beta played a major role in positive classification advantage (PCA) within the 100–200 and 300–400 ms intervals, whereas theta was important within the 200–300 ms interval. We propose that the beta band modulated the neutral and emotional face classification process, and that the theta band modulated for happy and sad face classification.

Original languageEnglish
Article number659
JournalFrontiers in Human Neuroscience
Volume11
DOIs
Publication statusPublished - 11 Jan 2018

Keywords

  • Brain oscillation
  • Correlation
  • Positive classification
  • Reaction times
  • Time intervals

Fingerprint

Dive into the research topics of 'Positive classification advantage: Tracing the time course based on brain oscillation'. Together they form a unique fingerprint.

Cite this