EEG Signal Analysis for Relaxation Detection
Objective
This study aims to develop an automated EEG-based system that detects human relaxation levels using spectral features and SVM classification for accurate, real-time stress monitoring and brain–computer interface applications.
Abstract
This study proposes an automated system for detecting human relaxation states using EEG signals, combining spectral analysis with machine learning techniques. The raw EEG data undergo preprocessing through bandpass and notch filtering, followed by segmentation into overlapping epochs. Power Spectral Density is estimated using Welch’s method to extract features such as alpha, beta, and gamma band powers, along with the alpha-to-beta ratio. Based on these spectral features, trials are categorized into relaxed, neutral, and stressed states. A Support Vector Machine (SVM) classifier is employed to perform the classification, and its performance is assessed using accuracy metrics and a confusion matrix. The results demonstrate that the proposed method can reliably differentiate between relaxation levels, making it suitable for real-time stress monitoring and applications in brain–computer interfaces.
KEYWORDS: EEG, Relaxation Detection, Power Spectral Density, Support Vector Machine, Brain–Computer Interface.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2022b or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
