Thinking in Emoticons: A Peek into the Future of Brain-Computer Interaction

This paper presents an investigation into the application of Brain-Computer Interface (BCI) technology for emoji selection by analyzing electroencephalographic (EEG) signals. EEG data was captured to interpret neural activity, with a Support Vector Machine (SVM) classifier employed for accurate emoji recognition. The system demonstrated a classification accuracy of 93.28% while implementing advanced signal processing techniques that reduced noise and improved real-time performance. This research highlights the viability of thought-driven emoji selection and its implications for advancing accessible communication technologies and immersive virtual environments.