• Manisha Shukla
• Prof. Kavita Kamerikar

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. Drowsiness can also be a result of your mental, emotional, or psychological state. It can come from any event or thought that makes you feel frustrated, angry, or nervous. Drowsiness 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 review of machine learning method for drowsiness detection using EEG signals.

EEG, Emotion, Drowsiness, Drowsiness , Machine Learning, E-healthcare

"Machine Learning Techniques for Drowsiness Detection : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.g183-g187, January-2023, Available :http://www.jetir.org/papers/JETIR2301626.pdf

"Machine Learning Techniques for Drowsiness Detection : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppg183-g187, January-2023, Available at : http://www.jetir.org/papers/JETIR2301626.pdf