1. Basic Details of the Team
and Problem Statement
Problem Statement Title: Brainwave-Controlled Smart
Automation System
Team Name: CircuitBaaZ
Team Leader Name: Ankur Rawat
Mentor Name:
Institute Name: IIMT College of Engineering , Greater Noida
School of Computer
Science and
Engineering (SCSE)
2. Idea/Approach Details
An EEG-based Mind Controller automates device control using non-invasive
brainwave signals. By detecting brain activity patterns such as concentration,
users can operate smart devices hands-free. This solution enhances
accessibility for individuals with disabilities and offers a futuristic approach to
smart automation.
Key Features:
● Non-invasive EEG signals control devices via microcontrollers.
● Hands-free operation: Controls lights, fans, prosthetics, and drones.
● Accessibility-focused: Empowers individuals with physical limitations.
● Real-time processing: Ensures immediate device response.
● Adaptable & scalable: Suitable for home automation, healthcare, and
industry.
2
EEG-based BCI using BioAmp EXG Pill,
ESP32 (P2P), Python, Matplotlib, Pygame,
NumPy, SciPy, TensorFlow, and PySerial. It
processes and visualizes brain signals,
classifies mental states (focus, relaxation)
with ML, and enables device control.
Designed for applications like prosthetics
and smart automation, it enhances real-
time brain-computer interaction..
TECH STACK
3. Idea/Approach Details
Use Cases
Smart Device Control: Use brain signals to control
appliances like lights, fans, or even security systems,
enhancing accessibility for people with physical
disabilities.
Prosthetic Limb Control: Enable control of prosthetic
limbs using brain waves for more natural movement and
interaction.
Mental State Monitoring: Track mental states like focus,
relaxation, or stress, useful in healthcare, meditation, or
productivity applications
.
Drone Control: Operate drones with mental commands,
offering hands-free operation for users with mobility
challenges.
Healthcare & Wellness: Provide feedback on mental well-
being and suggest relaxation techniques.
3
Challenges during development
During the development of Intellect Bridge, several
challenges were encountered:
1. Signal Noise: EEG signals are often noisy, requiring
effective filtering and preprocessing to obtain
accurate data.
2. Real-Time Processing: Managing low-latency data
processing for visualization and control was
complex.
3. Hardware Integration: Seamless communication
between EEG devices and the software required
significant fine-tuning.
4. ML Model Accuracy: Classifying mental states with
precision demanded robust feature extraction and
model training.
5. User Comfort: Designing a comfortable yet effective
EEG headset was tricky.
4. SDG GOALS MAPPED WITH SOLUTION
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PROBLEM
STATEMENT
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SDG8 SDG9 SDG10 SDG11 S
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Hands-free
device control
using EEG
signals for
accessibility
and
automation."
Enhances
healthcare
through
prosthetic
control,
mental state
monitoring,
and stress
management.
Improves
workplace
accessibility
and drives
innovation
in brain-
computer
interface
(BCI)
technology.
Advances
smart
automatio
n and
healthcare
with real-
time
brainwave-
driven
device
control.
Empowers
individuals
with
disabilities
by enabling
hands-free
control of
devices
and
prosthetics.
Supports
smart
home
automation
and
security,
enhancing
urban
living.
5. Team Details
Member Name Pursuing
Degree
Year Contact Number Email ID
Kush Singhal Btech 3rd
Team Leader Name Pursuing Degree Year Contact Number Email ID
Ankur Rawat Btech 3rd 8700799392 ankurrawat2005@gmail.com
Mentor Name Designation Organization Name Contact Number Email ID
