Modeling and Optimization of Signals Using Machine Learning Techniques

4EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey

Bhoomika Patel H. C.^*, Ravikumar V. and Pavan Kumar S. P.

Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India

Abstract

The thinking skill of humans were based on their problem-solving skill, basically a cognitive process which includes improvement and thinking. It is important to find a different number of intelligences in students and children according to their ages. Normal strategies for estimating IQ anyway are presented to biasness issues. An elective answer for order IQ levels through an Electroencephalogram (EEG)-based insight classifier model is proposed. Multiple tests are done to classify a ratio of IQ including multiple datasets using machine learning classification via neural network. Support Vector Machine (SVM), Artificial Neural Networks (ANN), and various division calculation and classifiers are utilized to distinguish the degrees of knowledge of people groups of different ages utilizing distinctive tests. In this survey, we investigate various approaches utilized for IQ test as a study. From this, we would be able to understand the various methodologies used for intelligence level estimation and furthermore used to distinguish the downsides of existing framework.

Keywords: Electroencephalogram (EEG), intellectual quotient (IQ), machine learning, nervous system, alpha band, beta band, support vector machine (SVM), artificial neural networks ...