Zero-Cost Simulated Brain-Computer Interface for Binary Classification using EEG Data

This work presents a prototype simulation of a Brain-Computer Interface (BCI) system capable of classifying EEG signals into binary outputs (Yes/No). Built using Streamlit and Python, this setup enables real-time analysis, visualization, and classification of EEG data without requiring hardware. The tool is intended as a research and education aid, with potential for further development into low-cost neuroadaptive systems.