A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Pramod Gaur, Harsh Gupta, Anirban Chowdhury, Karl Mc Creadie, Ram Bilas Pachori, Hui Wang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain-computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients' data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients' data set for left-versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa (κ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.
Original languageEnglish
Article number4002709
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
DOIs
Publication statusPublished - 18 Jan 2021
Externally publishedYes

Bibliographical note

Publisher Copyright: © 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Brain-computer interface (BCI)
  • common spatial patterns (CSPs)
  • electroencephalography (EEG)
  • linear discriminant analysis (LDA)
  • motor imagery (MI)
  • neurorehabilitation

Fingerprint

Dive into the research topics of 'A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI'. Together they form a unique fingerprint.

Cite this