A Wearable EEG-Based Drowsiness Detection System with Blink Duration and Alpha Waves Analysis

Abstract

Drowsiness is one of the most prevalent causes of accidents in mining, driving and industrial activities carrying high personal risks and economic costs. For this reason, automatic detection of drowsiness is becoming an important application, and it is being integrated in a large variety of wearable and deeply embedded systems. Relevant effort has been spent in the past to quantify the drowsiness level from behavioral features exploiting eye tracking systems, dermal sensors or steering wheel movements. On the other hand, all these approaches lack of generality, they are highly intrusive and can only be applied in specific circumstances. A promising alternative approach is based on the extraction and processing of physiological features from the EEG using Brain Computer Interfaces (BCI). This work describes a wearable system capable of detecting drowsiness conditions and emitting alarms using only EEG signals, with three levels of alarm based on the blink duration and on the spectral power of alpha waves. This implementation aims to replace or complement the use of cameras and other sensors, extracting drowsiness information exploiting both behavioral and physiological features from EEG sensors only. The system was validated with 7 test subjects achieving detection accuracy of 85%, while being much more lightweight and compact than other state of the art methods.