A Novel Processing Model for P300 Brainwaves Detection

Authors

  • Wanus Srimaharaj Department of Information Technology, The International College, Payap University, Thailand https://orcid.org/0000-0002-5168-8787
  • Roungsan Chaisricharoen Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Thailand https://orcid.org/0000-0002-2492-7236

DOI:

https://doi.org/10.13052/jwe1540-9589.20815

Keywords:

Event-Related Potential, ERP, P300, Signal Processing, Bandpass Filter, fast Fourier transform, Feature Extraction, Classification, Machine Learning,, Decision Tree

Abstract

Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

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Author Biographies

Wanus Srimaharaj, Department of Information Technology, The International College, Payap University, Thailand

Wanus Srimaharaj received Ph.D. in Computer Engineering from Mae Fah Luang University, Thailand. He is currently a lecturer at Payap University, Thailand. His research interests are bioinformatics, neuroscience, brain-computer interface, and machine learning.

Roungsan Chaisricharoen, Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Thailand

Roungsan Chaisricharoen received B.Eng. and M. Eng. in Computer Engineering, and Ph.D. in Electrical and Computer Engineering from King Mongkut’s University of Technology Thonburi. He is currently a lecturer at School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand. His research interests are analogue circuit and IC design, continuous-time active filter, compensation/optimization techniques, data and computer communication, networking, active inductor simulation, design and analysis of experiments, and computational intelligence.

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Published

2021-11-21

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