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    Enhancing Speech Impairment Support: Designing an EEG-Based BCI System for Turkish Vowel Recognition
    (Int Information & Engineering Technology Assoc, 2024) Haltas, Kadir; Erguzen, Atilla
    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 '& Idot;,' 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 EEGbased 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.