EEG based early detection of Autism by using KNN or SVM
Objective
The primary objective of this study is to develop a robust and accurate system for early detection of Autism Spectrum Disorder (ASD) using Electroencephalography (EEG) data, and to explore the effectiveness of two well-established machine learning algorithms, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), in classifying individuals with and without ASD based on EEG signals.
Abstract
This study aims to pioneer a reliable and precise system for the early identification of autism spectrum disorder (ASD) through the utilization of Electroencephalography (EEG) data, employing the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms. ASD is a complex neurodevelopmental condition that can greatly benefit from early diagnosis and intervention. By harnessing the power of EEG signals, which provide valuable insights into brain activity, this research seeks to establish a robust framework capable of distinguishing individuals with ASD from those without. The KNN and SVM classifiers will play a pivotal role in this classification task, leveraging EEG data patterns to make accurate predictions. If successful, this endeavour could open new avenues for the timely diagnosis and intervention of ASD, potentially improving the quality of life for affected individuals and their families.
Keywords: K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, of autism spectrum disorder (ASD).
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Specifications
Software: Matlab 2020a 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 an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation 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.
