Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 36 | Slow Wave Sleep Enhancement System RFA |
Aidan Stahl Kavin Bharathi Vikram Chakravarthi |
Hossein Ataee | design_document1.pdf proposal1.pdf proposal2.pdf proposal3.pdf |
Sound Sleep |
| # Slow Wave Sleep Enhancement System ## Disclaimer: We are assisting Team 05 - Acoustic Stimulation to Improve Sleep who presented during the first class lecture with this project # Team Members: - Kavin Bharathi (kavinrb2) - Aidan Stahl (ahstahl2) - Vikram Chakravarthi (vikram5) # Problem: Many common neurological conditions like Alzheimer’s disease, depression, and memory issues are associated with patients receiving lower quality of sleep. Specifically, these issues often stem from a lack of a specific type of sleep known as slow wave sleep (SWS). As individuals age, sleep disorders and other sleep-related issues lead to a lack of overall sleep. As a result, the amount of time an individual spends in SWS and the quality of SWS they experience typically declines with age, contributing to many of the issues mentioned above. # Solution: Describe your design at a high-level, how it solves the problem, and introduce the subsystems of your project. Our team is trying to improve sleep quality using a wearable device that is non-invasive and cost effective. This device will record EEG waves and then detect when the user is in Slow Wave Sleep (SWS) using the aid of specialized software. Once the user enters SWS, the system emits carefully timed bursts of pink noise through an auditory interface to enhance slow wave activity and extend its duration. This project is being done for the “Team 05 - Acoustic Stimulation to Improve Sleep” proposal by Maggie Li, Nafisa Mostofa, Blake Mosher, Presanna Raman. Currently, our sponsors have a wearable headset that measures how much time is spent in SWS and a “Cyton + Daisy Biosensing PCB” to process incoming signals. This board costs $2,500, and we are aiming to design an alternative, cheaper PCB within the class budget of $150. Providing a cheaper alternative that offers similar functionality is what makes our project unique and patentable. # Solution Components: ## EEG Leads - EEG Leads are conductive electrodes, small metal disks, that are placed on the scalp. These electrodes measure small voltage differences generated by electrical activity produced by neurons in the brain. ## MCU/EEG Wave Detection System - The MCU/EEG wave detection system is used to detect the analog EEG waves from the EEG headband, amplify the signal (the EEG waves are very low voltage, so amplification will be necessary), digitize them, and transmit those signals to a computer for further processing to detect SWS. ## Computer/Software - Utilize YASA, open-source command-line tool, to analyze EEG signals - Python script to utilize command-line tool while EEG data is being collected - Script also starts the process of playing pink noise once SWS is detected - Interactive UI that allows user to visualize EEG data ## Audio Source - An audio source will be used to play pink noise after the user enters SWS. # Criterion For Success: - Playing pink noise after detecting SWS signal with minimal delay - Correctly classify SWS with good accuracy - Ensure wearable device is comfortable for user through survey metrics |
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