Design and Implementation of an Electroencephalography System for Emotion Recognition

Emotions directly affect essential functions in human cognition, decision-making, and interactions. Any change in a person’s emotions can lead to different patterns in brain wave signals. Electroencephalography (EEG) is one of the key methods used to measure the brain's electrical activity. Emotion identification through EEG data has become a crucial element of human-computer interaction. Recently, the increasing need for monitoring brain activity has been measured by EEG devices, and the high price of these devices, which are not accessible outside of the laboratory for personal use, has prompted the development of low-cost wearable EEG devices. This paper introduces the design and implementation of an EEG system using available and low-cost components. This EEG system applies to emotion identification using the STM32F103C8T6 microcontroller, two-stage amplifiers, and filtering by bandwidth of (0.5-48) Hz. These data are logged and processed using MATLAB 2024, where Power Spectral Density (PSD) is applied for feature extraction and then the Support Vector Machine (SVM) for emotion detection, such as boredom, calm, fear, and happiness. Results show that this system can achieve an accuracy of a range between (79-83) % for the four emotion classifications. This study concludes by summarizing the practical importance of Electroencephalography (EEG) signals in emotion recognition, focusing on its potential for future applications.