Statistical Channel Selection Method for Detecting Drowsiness Through Single-Channel EEG-Based BCI System

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.