EEG Classification-based Comparison Study of Motor-Imagery Brain-Computer Interface

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

2 Citations (Scopus)

Abstract

For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021
EditorsHakim Bendjenna, Abdallah Meraoumia, Mohamed Amroune, Laouar Med Ridda, Nouri Boumaza, Salem Abdelmalek, Adel Ouannas, Lakhdar Laimeche
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441711
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021 - Tebessa, Algeria
Duration: Sept 21 2021Sept 22 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021

Conference

Conference2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021
Country/TerritoryAlgeria
CityTebessa
Period9/21/219/22/21

Keywords

  • Brain computer interface
  • EEG signal classification
  • Electroencephalogram (EEG)
  • KNN
  • LDA
  • Motor imagery BCI
  • SVM

ASJC Scopus subject areas

  • Analysis
  • Mathematics (miscellaneous)
  • Applied Mathematics
  • Computational Mathematics
  • Computational Theory and Mathematics

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