Abstract:
"Human emotions are a complex part of our inner experiences. It plays an essential role in
influencing our interactions and overall well-being. However, accurately identifying these
emotions poses a considerable challenge. Traditional methods often rely on external cues like facial expressions, making them susceptible to manipulation and lacking depth. The
groundbreaking convergence of EEG signals and artificial intelligence (AI) provides a
transformative solution, revealing insights into the neural underpinnings of our emotional
states. Patient well-being goes beyond physical health in healthcare systems, such as in public hospitals or clinics. Mental health plays a significant role in overall recovery and quality of life. However, identifying and addressing mental health issues like stress and depression can be challenging, especially in busy medical environments and in communities where parents may not be able to locate their children’s mental well-being. Also, healthcare providers may struggle to identify patients' mental health, because patients may not always express their emotional state openly. To overcome these challenges, communities and healthcare providers increasingly integrate Emotion Recognition Systems (ERS) into their patient care processes. However, many existing Emotion Recognition Systems face some limitations in accurately identifying the specific emotions of patients.
To address the above issue, this research project is called Emotion X, an innovative Emotion Recognition System that uses EEG signals and AI to provide real-time emotion identification for patients."
