Three-class EEG-based motor imagery classification using phase-space reconstruction technique

Ridha Djemal, Ayad G. Bazyed, Kais Belwafi, Sofien Gannouni, Walid Kaaniche

    Research output: Contribution to journalArticlepeer-review

    54 Scopus citations


    Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies.

    Original languageBritish English
    Article number36
    JournalBrain Sciences
    Issue number3
    StatePublished - Sep 2016


    • Brain-computer interface (BCI)
    • Electroencephalogram EEG
    • Motor imagery (MI)


    Dive into the research topics of 'Three-class EEG-based motor imagery classification using phase-space reconstruction technique'. Together they form a unique fingerprint.

    Cite this