Toward Safer Vehicular Transit: Implementing Deep Learning on Single Channel EEG Systems for Microsleep Detection

Abstract

Technological interventions are becoming commonplace in everyday vehicles. But utilization of biosignals that can enhance the overall driving experience is still limited. Microsleep is one such issue that needs intervention, owing to the difficulty in its detection and social acceptance of using wearable BCI devices during transit. Microsleep is a short duration of sleep that lasts from few to several seconds. It could occur unconsciously without the person in context realizing it. This, therefore, happens before the deep sleep and could also occur when performing critical tasks such as driving on a highway. By using modern-day advancements in Internet of Things (IoT) and Machine Learning, we can provide efficient solutions to prevent accidents due to microsleep during vehicular transit. However, it is noteworthy that distinguishing microsleep using a single channel system is a challenge. We have explored this using datasets provided by International BCI Competition Committee. Given the fact that the participants’ values might not match the exact scenario, approaches for exploiting transitory phases using ANN/CNN have been developed and discussed in this paper. Transitory phases could include Wakefulness <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula> Non-Rapid Eye Movement-1 phase (NREM-1). Results show ≈95% increase in mean statistical agreements, which are represented by kappa values (CNN NREM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1~\rightarrow $ </tex-math></inline-formula> CNN Transition) and ≈77% increase in mean kappa (ANN NREM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1~\rightarrow $ </tex-math></inline-formula> ANN Transition). Hence, this work gives an initial indication whether classifiers trained on night sleep data can be used for microsleep detection in more real-world scenarios.