Motor Imagery Classification Using Rough Neural Network

Authors

  • J Anila Maily Research Scholar, (Part-time internal) (Register Number: 12336), Dept of Computer Science, Sadakathullah Appa College, Rahmath Nagar, Tirunelveli. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamil Nadu, India
  • C Velayutham Head and Associate Professor, Department of Computer Science, Aditanar College of Arts and Science Tiruchendur Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, TamilNadu, India.
  • M Mohamed Sathik Principal, Sadakathullah Appa College, Rahmath Nagar, Tirunelveli -627011, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamil Nadu,

DOI:

https://doi.org/10.23755/rm.v44i0.902

Keywords:

BCI, EEG, Motor Imagery, RNN

Abstract

Brain Computer Interface is a system which provides a communication channel between the user and a computer without using the normal neuromuscular pathways. With BCI a user will be able to communicate with the mind. In a BCI system the brain activities are measured using EEG acquisition system. The acquired brain signals are analyzed and classified to identify the user’s intention. Motor imagery BCI works by making the user imagine their body parts without actually moving it. Prominent features are extracted from the acquired brain signals and the extracted features are classified to find the motor imagery performed by the user. This study uses datasets are provided by the Dr. Cichocki's Lab (Lab for Advanced Brain Signal Processing). We propose the Rough Neural Network (RNN) for Motor imagery classification. The experimental results show that RNN classifier gives higher accuracy than Backpropagation Classifier

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Published

2022-12-11

Issue

Vol. 44 (2022): Special Issue: Recent Trends in Mathematics and its Applications

Section

Volume 45-2023

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