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

Electronic Replacement for COVID-19 Building Monitors @ UIUC

Patrick McBrayer, Zewen Rao, Yijie Zhang

Featured Project

Team Members: Patrick McBrayer, Yijie Zhang, Zewen Rao

Problem Statement:

Students who volunteer to monitor buildings at UIUC are at increased risk of contracting COVID-19 itself, and passing it on to others before they are aware of the infection. Due to this, I propose a project that would create a technological solution to this issue using physical 2-factor authentication through the “airlock” style doorways we have at ECEB and across campus.

Solution Overview:

As we do not have access to the backend of the Safer Illinois application, or the ability to use campus buildings as a workspace for our project, we will be designing a proof of concept 2FA system for UIUC building access. Our solution would be composed of two main subsystems, one that allows initial entry into the “airlock” portion of the building using a scannable QR code, and the other that detects the number of people that entered the space, to determine whether or not the user will be granted access to the interior of the building.

Solution Components:

Subsystem #1: Initial Detection of Building Access

- QR/barcode scanner capable of reading the code presented by the user, that tells the system whether that person has been granted or denied building access. (An example of this type of sensor: (https://www.amazon.com/Barcode-Reading-Scanner-Electronic-Connector/dp/B082B8SVB2/ref=sr_1_11?dchild=1&keywords=gm65+scanner&qid=1595651995&sr=8-11)

- QR code generator using C++/Python to support the QR code scanner.

- Microcontroller to receive the information from the QR code reader and decode the information, then decide whether to unlock the door, or keep it shut. (The microcontroller would also need an internal timer, as we plan on encoding a lifespan into the QR code, therefore making them unusable after 4 days).

- LED Light to indicate to the user whether or not access was granted.

- Electronic locking mechanism to open both sets of doors.

Subsystem #2: Airlock Authentication of a Single User

- 2 aligned sensors ( one tx and other is rx) on the bottom of the door that counts the number of people crossing a certain line. (possibly considering two sets of these, so the person could not jump over, or move under the sensors. Most likely having the second set around the middle of the door frame.

- Microcontroller to decode the information provided by the door sensors, and then determine the number of people who have entered the space. Based on this information we can either grant or deny access to the interior building.

- LED Light to indicate to the user if they have been granted access.

- Possibly a speaker at this stage as well, to tell the user the reason they have not been granted access, and letting them know the

incident has been reported if they attempted to let someone into the building.

Criterion of Success:

- Our system generates valid QR codes that can be read by our scanner, and the data encoded such as lifespan of the code and building access is transmitted to the microcontroller.

- Our 2FA detection of multiple entries into the space works across a wide range of users. This includes users bound to wheelchairs, and a wide range of heights and body sizes.