Development of Personalized Sleep Induction System based on Mental States

TL;DR

This paper presents a personalized sleep induction system that analyzes mental states via EEG and auditory stimuli to effectively promote sleep, achieving high accuracy in sleep stage classification and successful sleep induction in most participants.

Contribution

It introduces a novel system combining EEG analysis and tailored auditory stimulation to enhance sleep induction, with a high classification accuracy and practical effectiveness.

Findings

Sleep stage classification accuracy of 94.7%

18 out of 20 participants successfully fell asleep

Effective differentiation of mental states for personalized stimulation

Abstract

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.