Automated Alphabet Detection from Brain Waves
Authors: 
Christopher Dargan, Francis Nweke, Md Abdullah Al Hafiz Khan, Abm Adnan Azmee, Yong PeiAuthors Info & Claims
Christopher Dargan, Francis Nweke, Md Abdullah Al Hafiz Khan, Abm Adnan Azmee, Yong PeiAuthors Info & ClaimsPages 247 - 252
Abstract
Brain-computer interfaces (BCIs) offer a novel method of converting brain activity into valuable data. This study investigates the use of electroencephalogram (EEG) signals for recognizing brainwave signals to record human thoughts. Our research focuses on using EEG signals to facilitate communication for people who have traditionally lost their ability to communicate. We concentrated on utilizing machine learning algorithms to decode these EEG data, especially using Support Vector Machines, which produced useful results in classification attempts. Our findings reveal that it is possible to detect individual letters from EEG signals using noninvasive approaches, demonstrating the potential of thought-to-text technology to help a wide range of people and applications.
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- Automated Alphabet Detection from Brain Waves
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April 2024
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Published: 27 April 2024
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