Authors - Janina Odette S. Vidallon, Apolinar P. Datu, Dominic T. Urgelles, Aljen B. Cabrera, Erika Joy F. Lagos, Lady Anne R. Logdat, Shenclaire A. Galero, Ericka Jean M. Amparo Abstract - Brain-computer interface systems can help people who are unable to communicate due to paralysis or severe motor disabilities. In this work, we im plemented an EEG-based P300 speller that allows users to select characters by focusing on a visual stimulus.The system functions by means of the P300 signal that appears when the user identifies their target character. We developed a com plete pipeline that includes feature extraction, machine learning model classifi cation, and preprocessing of EEG data. The system was tested using the BNCI Horizon 2020 P300 dataset, and the results showed that character selection accu racy ranged from 82% to 86%.Random Forest performed better compared to other classifiers in our implementation. The system was designed in a modular way so that future improvements can be added easily. This implementation shows that EEG-based communication systems can be developed using accessible tools and can support basic communication for people with severe motor impairments.
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