Enhancing Speech Impairment Support: Designing an EEG-Based BCI System for Turkish Vowel Recognition Kadir Haltas 1* , Atilla Erguzen 2 1 Department of Computer Technologies, Vocational High School, Nevsehir Hacı Bektaş Veli University, Nevşehir 50300 Turkey 2 Department of Computer Engineering, Engineering Faculty, Kırıkkale University, Nevşehir 71450, Turkey Corresponding Author Email: haltaskadir@nevsehir.edu.tr Copyright: ©2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.18280/ts.410310 ABSTRACT Received: 23 November 2023 Revised: 19 February 2024 Accepted: 8 May 2024 Available online: 26 June 2024 Brain-Computer Interfaces (BCI) have garnered significant attention as a technology that enables individuals to interact with their surroundings using brain activity. In the realm of BCIs, EEG-based systems offer a non-invasive and cost-effective means of monitoring brain activity. This study focuses on EEG-based BCIs and, in particular, aims to recognize Turkish vowel articulation intentions from EEG signals in healthy individuals. Turkish vowels, specifically 'A,' 'E,' and 'İ,' were chosen for their high frequency of use in the language. The study explores two distinct BCI system designs, one employing the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms and the other utilizing the Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) algorithms. The results indicate that the second system, employing DWT and SVM, achieved a higher accuracy rate (80.2%) compared to the first system (67.7%), showcasing the superior performance of the DWT algorithm. This research could be a significant step towards improving the quality of life for individuals with speech impairments. The ability of EEG- based BCI systems to recognize the intentions of Turkish vowel articulation could aid these individuals in expressing their thoughts and intentions. Ultimately, this study contributes to the ongoing efforts to harness technology in ways that can significantly improve the lives of individuals with speech impairments. Keywords: Brain-Computer Interfaces (BCI), EEG, vowel, Support Vector Machine (SVM), Common Spatial Patterns (CSP), signal 1. INTRODUCTION Various devices, software, and systems are designed to enhance the quality of life for individuals, thanks to the contributions of scientific advancements. BCI are among these technological developments. The interpretation and understanding of brain signals using external devices and software systems has become one of the most popular research areas among researchers over the last 20 years [1-5]. These systems are known as BCI. Essentially, BCIs enable individuals to interact with their surroundings or use tools and systems solely through brain activity [2, 5-7]. Various methods are preferred for monitoring brain activity in BCI systems, including PET, MEG, EEG, NIRS, and fMRI [2, 8]. Due to its low cost and ease of application compared to other medical techniques, research on EEG-based BCI systems has gained momentum [8]. The invasive method involves a medical procedure with a high risk for life. In the non-invasive method, a healthier structure is established by observing electrical potential differences between neurons using electrodes placed on the individual's scalp [9]. This study is implemented on an EEG-based BCI system. One of the distinguishing features among EEG-based BCI systems is the criteria used to evaluate brain signals. In EEG- based BCI systems, researchers typically perform observations of ERP (Event-Related Potential), SCP (Slow Cortical Potentials), VEP (Visual Evoked Potentials), and Sensory Motor Rhythm [10, 11]. • ERP: Electrical potential differences that occur in brain signals after a stimulus are examined [12, 13]. Different wave forms such as P100, N170, and P300 are evaluated in BCI systems in the literature. Each of these ERP components is examined in brain signals for different purposes [4, 8, 13]. Among these components, the P300 component is the most frequently used in ERP-based BCIs [12, 14, 15]. P300 spellers [4] are the most commonly used application models in the P300 ERP wave form [3, 13]. • VEP: The overlap between the signal fluctuation frequency and the stimulus frequency is observed in brain signals [16]. VEP-based BCI systems are used for steering activities with a limited number of commands, such as controlling a toy car [16]. In the literature, various VEP-based BCI systems, like c-VEP and SSVEP, exist. In systems with complex command sets, it can be challenging for users to maintain focus on the stimulus within a limited time [17]. • SMR: The changes in the frequencies of brain signals are examined [18]. SMR observations are often encountered in Motor Imagery BCI systems [6, 13, 19]. It is found in areas such as helping people with physical movement difficulties Traitement du Signal Vol. 41, No. 3, June, 2024, pp. 1205-1213 Journal homepage: http://iieta.org/journals/ts 1205