• Rahul
• Harshita Kamboj
• Sarvendra Kumar

Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. EEG signals can reflect activities of the human brain and represent different emotional states. Stress is a feeling of emotional or physical tension. It can come from any event or thought that makes you feel frustrated, angry, or nervous. Mental stress has become a social issue and could become a cause of functional disability during routine work. A machine learning (ML) framework is effective for electroencephalogram (EEG) signal analysis. This paper reviews of stress emotion recognition from EEG for e-healthcare applications.

EEG, Emotion, Stress, Machine Learning, E-healthcare.

"Stress Emotion Recognition from EEG for E-healthcare Applications : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.j589-j593, June-2022, Available :http://www.jetir.org/papers/JETIR2206975.pdf

"Stress Emotion Recognition from EEG for E-healthcare Applications : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppj589-j593, June-2022, Available at : http://www.jetir.org/papers/JETIR2206975.pdf