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

Abstract

International audienceDue to its major safety applications, including safe driving, mental fatigue estimation is a rapidly growing research topic in the engineering field. Most current mental fatigue monitoring systems analyze brain activity through electro-encephalography (EEG). Yet eye blink analysis can also be added to help characterizing fatigue states. It usually requires the use of additional devices, such as EOG electrodes, uncomfortable to wear, or more expensive eye trackers. However, in this article, a method is proposed to evaluate eye blink parameters using frontal EEG electrodes only. EEG signals, which are generally corrupted by ocular artifacts, are decomposed into sources by means of a source separation algorithm. Sources are then automatically classified into ocular or non-ocular sources using temporal, spatial and frequency features. The selected ocular source is back propagated in the signal space and used to localize blinks by means of an adaptive threshold, and then to characterize detected blinks. The method, validated on 11 different subjects, does not require any prior tuning when applied to a new subject, which makes it subject-independent. The vertical EOG signal was recorded during an experiment lasting 90 min. in which the participants’ mental fatigue increased. The blinks extracted from this signal were compared to those extracted using frontal EEG electrodes. Very good performances were obtained with a true detection rate of 89% and a false alarm rate of 3%. The correlation between the blink parameters extracted from both recording modalities was 0.81 in average

Passive BCI based on drowsiness detection: an fNIRS study

“…Another study [44] using a vision based method (i.e., eyelid closure degree) combined with EEG had demonstrated 87.5% and 70% accuracies for male and females, respectively. Also, in [45], EEG was combined with electrooculography (EOG) to acquire 89% accurate detection. Though the accuracies using ECD and EOG were higher than that of the current method, the results of the current study can further be improved by combining it with EEG, EOG, and/or an eye tracking system.…”

“…In the last decade, brain activity has been studied using several functional brain imaging modalities for steering, vigilance and drowsy states [34][35][36][37][38][39][40][41][42][43][44]. These studies showed the possibility of detection of vigilance [35], fatigue [42,43] and drowsiness [41,44,45]. Mostly, they considered the neural correlates necessary for detection of drowsiness [35,[40][41][42][43][44][45][46].…”

“…These studies showed the possibility of detection of vigilance [35], fatigue [42,43] and drowsiness [41,44,45]. Mostly, they considered the neural correlates necessary for detection of drowsiness [35,[40][41][42][43][44][45][46]. However, to avoid false alerts, the characteristics of the hemodynamic response due to drowsiness, along with the neuronal response, should also be studied.…”

Selection of the Most Relevant Physiological Features for Classifying Emotion

“…Unfortunately eye blinking rate was not available in MANHOB-HCI database. One possibility would be to identify it from EEG frontal signal (Roy et al, 2014). Features extracted from GSR show a significant correlation with arousal for both DEAP and MANHOB databases.…”

Single channel wireless EEG device for real-time fatigue level detection

“…Some 978-1-4799-1959-8/15/$31.00 @2015 IEEE physiological features, like brain wave, eye-blinking frequency, heart rate and blood pressure, are verified to have high correlation with drivers' fatigue level. By using specific devices to recording these physiological signals, we can monitor drivers' fatigue state and reduce traffic accidents caused by fatigue driving [4][5][6][7][8].…”