A Novel Processing Model for P300 Brainwaves Detection

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.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

“…Researchers engage in problem identification with ML to uncover new insights by classifying data clusters. Moreover, ML was adopted as an online learning platform through pattern recognition [15], speech transcription [16], bioinformatics classification [17], and structured data analysis [18]. Basically, ML offers a student performance tracking and evaluation mechanism.…”

An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems

“…Before digitalization at 240 Hz, signals were bandpass filtered from 0.1 to 60 Hz [15]. The dataset was described in further detail in the BCI competition [30]. The classification of problems addressed is as follows: After stimulation and recording a 64-channel EEG signal for each subject, we wish to predict whether or not such a signal has a P300 component.…”

“…A combination of optimization and pattern recognition methods was used for P300 detection, tested on five datasets, with an accuracy of 96.70% as the best performance [29]. The decision tree was used as a classifier on an open dataset from an expert psychologist association, with an accuracy of 99.68% obtained [30].…”