Feature extraction and classification of EEG signal for different brain control machine

Sheikh Md Rabiul Islam, Ahosanullah Sajol, Xu Huang, Keng Liang Ou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). All these methods are compared with other work that have done with other datasets. The proposed work is obtained 95.21% accuracy 98.95% sensitivity for SVM and k-NN is 90.88% and ANN is 94.31%. The performance results have shown higher enough than all others.

Original languageEnglish
Title of host publication2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509029068
DOIs
Publication statusPublished - Mar 6 2017
Event3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 - Dhaka, Bangladesh
Duration: Sep 22 2016Sep 24 2016

Other

Other3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
CountryBangladesh
CityDhaka
Period9/22/169/24/16

Keywords

  • Acuraccy
  • ANN
  • EEG datasets
  • k-NN
  • SVM

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Signal Processing
  • Electrical and Electronic Engineering

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