• Alok Meshram
• Dr. Shailja Shukla

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. Drowsiness has become a leading sleeping disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiologic signals from the healthy controls when they are exposed to positive and negative. Drowsiness is a common reason for an increase in accident cases worldwide. This paper proposed a SVM machine learning method for drowsiness detection using EEG signal. The algorithm first extracts features from EEG signals and classifies drowsiness using machine learning techniques.

EEG, Drowsiness, Machine Learning, E-healthcare

"A SVM Machine Learning Method for Drowsiness Detection Using EEG Signal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.i1-i6, April-2022, Available :http://www.jetir.org/papers/JETIR2204801.pdf

"A SVM Machine Learning Method for Drowsiness Detection Using EEG Signal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppi1-i6, April-2022, Available at : http://www.jetir.org/papers/JETIR2204801.pdf