Mental drowsiness evaluation techniques using EEG signals: a review

Electroencephalography (EEG) signals have a complex nature and their interpretation needs advanced techniques. Numerous approaches were employed to interpret EEG in the context of detecting driver drowsiness. This review identifies noteworthy methodologies from the literature to show the new opportunities for researchers. First, major databases were searched to identify relevant papers published in scientific journals. Each study's EEG channels, algorithms and classification procedures were analyzed. It was observed that support vector machine (SVM) and convolutional neural networks (CNN) were used in most of the studies. The analysis indicates that drowsiness negatively impacts performance, leading to increased hazardous situations for individuals.

Keywords:

Drowsiness , Electroencephalography , support vector machine , convolutional neural networks , Fatigue