Improving the performance of P300 BCI system using different methods
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(2020) 9:64
ORIGINAL ARTICLE
Improving the performance of P300 BCI system using different methods Islam A. Fouad2 · Fatma El‑Zahraa M. Labib1 · Mai S. Mabrouk2 · Amr A. Sharawy1 · Ahmed Y. Sayed3 Received: 16 May 2020 / Revised: 21 August 2020 / Accepted: 26 August 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS, or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible, and that it might serve useful functions. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. BCI systems include machine learning algorithms (MLA). Their performance depends on the feature extraction and classification techniques employed. This work discusses the performance of different machine learning algorithms. First, a preprocessing step is introduced to the subjects to extract the important features before applying the machine learning algorithms. The presented algorithms are linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), and Bayesian linear discriminant analysis (BLDA). It is found that BLDA and SVMIV classifiers yield the highest performance for both subjects considered in our study. Keywords Brain Computer Interface (BCI) · P300 signal · Machine learning algorithms (MLA) · Linear discriminant analysis (LDA) · Support vector machine (SVM) · Linear regression (LREG) · Bayesian linear discriminant analysis (BLDA)
1 Introduction
* Islam A. Fouad [email protected] Fatma El‑Zahraa M. Labib [email protected] Mai S. Mabrouk [email protected] Amr A. Sharawy [email protected] 1
Biomedical Engineering and Systems Department, Cairo University, Giza, Egypt
2
Biomedical Engineering Department, MUST University, Giza, Egypt
3
Mathematics and Physics Engineering Department, El-Mataria, Helwan University, Cairo, Egypt
Amyotrophic lateral sclerosis or ALS is a progressive neurodegenerative disease that affects nerve cells in the brain and the spinal cord, which often leads to complete paralysis (www.alsa.org/news/media/quick-facts.html, 2012). ALS usually strikes people between the ages of 40 and 70. The incidence of ALS is two per 100,000 people, and it is estimated that about 30,000 people in the United States are living with ALS (www.alsa.org/news/media/quick-facts .html, 2012). As the disease progresses many assistive communication devices that have been once a necessity, may become ineffective (McCane et al. 2015). Brain Computer Interface (BCI) is one of the best research fi
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