Wireless-based portable EEG-EOG monitoring for real time drowsiness detection

Abstract

Drowsiness is one of the major risk factors causing accidents that result in a large number of damage. Drivers and industrial workers probably have a large effect on several mishaps occurring from drowsiness. Therefore, advanced technology to reduce these accidental rates is a very challenging problem. Nowadays, there have been many drowsiness detectors using electroencephalogram (EEG), however, the cost is still high and the use of this is uncomfortable in long-term monitoring because most of them require wiring and conventional wet electrodes. The purpose of this paper is to develop a portable wireless device that can automatically detect the drowsiness in real time by using the EEG and electrooculogram (EOG). The silver (Ag) conducting fabric consolidated in a headband used as dry electrodes can acquire signal from the user's forehead. The signal was sent via the wireless communication of XBee® 802.15.4 to a standalone microcontroller to analyze drowsiness using the proposed algorithm. The alarm will ring when the drowsiness occurs. Besides, the automatic drowsiness detection and alarm device yields the real-time detection accuracy of approximately 81%.

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

“…Ref. [1] propose a system placed on the forehead that allows for both cerebral and ocular activities' measurement. This system is intended to monitor one's drowsiness by detecting eye blinks along with power spectral measures.…”

Wearable and Flexible Textile Electrodes for Biopotential Signal Monitoring: A review

“…Therefore, the monitoring of alpha and beta waves would be useful for a variety of applications including cognitive neuroscience and perception. For instance, a headband with conductive fabrics and a custom-designed EEG amplifier was developed that detects drowsiness by measuring both alpha and beta waves [145]. Similarly, as illustrated in Figure 5a, Ag/AgCl-coated threads were integrated into a nylon headband through a seamless knitting technique, and its functionality was tested by monitoring alpha and beta waves; Figure 5b illustrates the associated power spectra plots [111].…”

“…For instance, using screen and stencil printing processes, an electrode network was fabricated and embedded on a headband and used for horizontal EOG acquisition (Figure 7a) [119]. Similarly, conductive fabrics used in a headband to measure EOG have been capitalized on in a drowsiness detection application [145]. Additionally, a silver-coated nylon textile was integrated into a headband and adapted for gesture recognition (Figure 7b) [80].…”

An algorithm for automatic detection of drowsiness for use in wearable EEG systems

“…The best performing features all came from channel CZ-A1, from which the top three features were selected. These features are center frequency, relative power in delta (0.5-4 Hz) and relative power in alpha (8)(9)(10)(11)(12)(13) and are discussed in the next section.…”

“…Picot et al [11] presented one such algorithm that combined two separate drowsiness detectors based on EEG and EOG channels individually. Arnin et al [12] also demonstrated a real-time drowsiness detector that used both EEG and EOG signals which were acquired using electrodes on the forehead. However, having a higher number of data channels also increases the computational complexity of the algorithm as well as that of the hardware system used to acquire these signals.…”