Emotion Recognition from Wearable EEG Devices Using Neural Networks

Recognizing emotions using wearable technology is an emerging field that leverages the breakthroughs in machine learning and neuroscience to conduct real-time analyses of human emotional states. This study explores the scope of wearable technology-based emotion recognition by incorporating EEG (Electroencephalogram) signals to capture the electrical activity of the brain. A Deep Neural Network (DNN) is deployed to sort emotions into three classes: positive, negative and neutral. Harnessing advanced feature extraction strategies for EEG processing brings about improved accuracy over conventional methods. The observations demonstrate that the proposed DNN-based model leads way to economical, non-invasive and mobile solution for affect monitoring. This research offers notable potential in fields like human-computer interaction, mental health tracking, and stress management at work. Scalable medical-grade equipment alternatives are attained by means of the easily accessible wearable devices, that could be used in much wider scale in real-world use cases. This study aims to offer a feasible strategy for emotion recognition using EEG through compact devices.