TY - GEN
T1 - The Statistical Characteristics of P3a and P3b Subcomponents in Electroencephalography Signals
AU - Azizah, Resfyanti Nur
AU - Ravienna, Karine
AU - Puspa, Lyra
AU - Akbar, Yudiansyah
AU - Valenza, Lula Kania
AU - Suwandi, Galih Restu Fardian
AU - Khotimah, Siti Nurul
AU - Haekal, Mohammad
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - EEG Features
KW - P3a
KW - P3b
UR - http://www.scopus.com/inward/record.url?scp=85174595312&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44195-0_18
DO - 10.1007/978-3-031-44195-0_18
M3 - Conference contribution
AN - SCOPUS:85174595312
SN - 9783031441943
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 210
EP - 220
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
Y2 - 26 September 2023 through 29 September 2023
ER -