Electroencephalogram (EEG) is an essential tool used to analyze the activities effectively and different states of the brain. Drowsiness is a short period state of the brain that is also… Click to show full abstract
Electroencephalogram (EEG) is an essential tool used to analyze the activities effectively and different states of the brain. Drowsiness is a short period state of the brain that is also called an inattentiveness state. Drowsiness can be observed during the transition from being awake state to a sleepy state. Drowsiness can reduce a person’s alertness that increases accidental risks when involved in their personal and professional activities like vehicle driving, operating a crane, mine blasts, and so on. Drowsiness detection (DD) has a significant role in preventing accidents. Neuroscience with artificial intelligence algorithms used to detect drowsiness is also popularly known as brain–computer interface (BCI) systems. Single-channel EEG BCIs are highly preferred for convenient use in real-time applications, even though there are many challenges in the actual experimental process. They are choosing the best single-channel and classifier. In this article, a novel channel selection approach is proposed for a single-channel EEG-BCI system by integrating the statistical characteristics of the available channel’s EEG signal. In addition to this, a deep neural network (DNN) classifier is developed using the stack ensemble process for better classification accuracy. Simulated-virtual-driving driver and physionet sleep analysis EEG datasets (PSAEDs) are used to test the proposed model. Subject-wise, cross-subject-wise, and combined subject-wise validations are also employed to improve the generalization capability of the proposed techniques in this article.
               
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