Decoding Imagined Speech Enhancing Learning Abilities Using EEG Signal Classification

Humans use communication as a powerful tool to express and convey feelings to each other. Communication is a vital tool for expressing feelings, but individuals with severe brain injuries often face challenges that hinder their ability to interact, leading to isolation and reduced quality of life. EEGbased brainwave analysis offers a promising solution for direct communication by interpreting user intentions. Our approach uses Convolutional Neural Networks (CNN) to monitor and improve vowel pronunciation in learners by analyzing EEG signals. The CNN model effectively classifies vowel pronunciations, helping students improve their speech. Compared to existing research, our method shows superior performance, with potential applications in education and speech therapy. Future work will explore its use in broader language learning and realtime interactive systems for personalized feedback.