The Statistical Characteristics of P3a and P3b Subcomponents in Electroencephalography Signals

Resfyanti Nur Azizah, Karine Ravienna*, Lyra Puspa, Yudiansyah Akbar, Lula Kania Valenza, Galih Restu Fardian Suwandi, Siti Nurul Khotimah, Mohammad Haekal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The P300 waveform is a common event-related potential (ERP) component in electroencephalography (EEG) signals used in clinical neurophysiology, brain-computer interface (BCI), and cognitive neuroscience research. Comprehensive documentation of P300 is needed to support research growth in those areas, especially for P300 subcomponents, P3a and P3b, which commonly used to quantify EEG characteristic. Therefore, this study aims to explore the quantitative characteristics of P3a and P3b subcomponents during the flanker test and Stroop task experiment. ERP waveforms were obtained from 16 healthy subjects to analyze the statistical features of the P3a and P3b subcomponents. This study also used decision trees for measuring the importance index. The result indicates that P3a can be defined as a prominent peak in the time window of 250–350 ms at the frontal area. Positive peak amplitude, area amplitude, mean amplitude, and the prominence of the positive peak have different distributions between group with P3a subcomponent and group without. Whereas P3b is characterized as a positive sloping peak in 300–700 ms at the parietal area. All statistical features influence P3a and P3b identification. However, features related to statistical characteristics of positive peaks have a greater importance index compared with others.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
EditorsLazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031441943
Publication statusPublished - 2023
Externally publishedYes
Event32nd International Conference on Artificial Neural Networks, ICANN 2023 - Heraklion, Greece
Duration: 26 Sept 202329 Sept 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14260 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd International Conference on Artificial Neural Networks, ICANN 2023


  • EEG Features
  • P3a
  • P3b


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