An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Saugat Bhattacharyya, Monalisa Pal, Amit Konar, D.N. Tibarewala

Research output: Contribution to journalArticle

11 Citations (Scopus)
2 Downloads (Pure)

Abstract

Feature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain–computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.
Original languageEnglish
Pages (from-to)90-98
JournalBiomedical Signal Processing and Control
Volume21
Early online date15 Jun 2015
DOIs
Publication statusPublished - Aug 2015

Keywords

  • electroencephalography (EEG)
  • interval type-2 fuzzy systems
  • motor imagery (MI)
  • Extreme Energy Ratio
  • Wrist movement and grasping

Fingerprint Dive into the research topics of 'An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement'. Together they form a unique fingerprint.

  • Profiles

    Cite this