EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers

Abstract

This chapter explores Wavelet Packet Decomposition (WPD) and Ensemble Classifiers to improve the accuracy and efficiency of P300 speller systems, which enable typing through EEG signals. This combination of Brain-Computer Interface (BCI) systems and P300 spellers represents a significant advancement in assistive technology, empowering individuals with severe motor limitations to communicate via brain signals. Traditional machine learning models, while effective, may suffer from overfitting and lower accuracy. To overcome these challenges, ensemble classifiers are utilized, leveraging diverse subsets of the dataset to enhance P300 recognition performance. The study employs multiscale principal component analysis for signal denoising, WPD for feature extraction, and ensemble models for BCI control systems. Through rigorous experimentation, the effectiveness of these strategies in improving spelling proficiency and reducing categorization errors is evaluated. The results demonstrate the potential of WPD and ensemble classifiers to enhance BCI-based communication systems, offering greater usability and effectiveness. The findings contribute valuable insights to the field of neurotechnology, promising advancements in improving the quality of life for individuals with movement disabilities. Overall, the use of ensemble learning models enhances the performance of the P300 speller, emphasizing the impact of WPD features combined with ensemble models on BCI recognition and paving the way for future assistive technology applications.