Real-Time Emotion Detection System using Emotive and ESP-32

This paper aims to develop an emotion detection system using Emotive and ESP-32. The system will use Emotive, an EEG headset that measures brain activity, to detect emotions in real-time. The data obtained from the Emotive will be transmitted wirelessly to an ESP-32 microcontroller, which will process the data and classify the emotion using machine learning algorithms. The ESP-32 will be programmed using the Arduino IDE and will use a wireless communication protocol such as Wi-Fi or Bluetooth to transmit the data. The machine learning algorithms will be trained using a dataset of EEG signals and corresponding emotions and will be optimized to classify emotions accurately. The system will have various applications in fields such as mental health, education, and entertainment. It could be used to detect the emotional state of a person and provide appropriate support or feedback. For example, in a classroom setting, the system could be used to detect when a student is feeling frustrated or confused and provide additional resources to help them understand the material better. Overall, this work has the potential to create a more empathetic and responsive environment, by detecting and responding to human emotions in real-time.