Handayani, Dini Oktarina Dwi and Yaacob, Hamwira Sakti and Osmani, Noor Mohammad and Attarbashi, Zainab
(2022)
A Real-Time Brain-Computer Interface (BCI) framework for sleep state stimulation using a deep-learning technique: proposal.
In: ICEPEE'22, 9-10th August 2022, IIUM (hybrid).
(In Press)
Abstract
Abstract— Sleep disturbance can cause mental illnesses such as depression, hypertension, metabolic syndrome, and cognitive impairment. To date, various methods have been proposed as intervention measures for sleep disturbance, including taking a short mid-day nap. Falling asleep depends on several external factors, such as the ambience, temperature, sound, and lighting. On top of that, the factors that affect the quality and period of falling asleep can be subjective. The attempt to provide feedback based on the configuration of those external factors is time-consuming. Additionally, if those external factors are incorrectly configured, the intended short nap as a solution may have the opposite effects. As such, research on real-time sleep analysis plays an important role. However, the current study on deep-learning techniques regarding the sleep analysis that can give real-time results is still scarce compared to the offline sleep analysis. Therefore, this study aims to design and develop a real-time BCI framework for sleep state stimulation.
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