A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

Hamza Baali, Aida Khorshidtalab, Mostefa Mesbah, Momoh J E Salami

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling's T2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.

Original languageEnglish
Article number2100108
JournalIEEE Journal of Translational Engineering in Health and Medicine
Volume3
DOIs
Publication statusPublished - 2015

Keywords

  • Brain-computer interface
  • channel selection
  • feature extraction
  • linear prediction
  • orthogonal transform

ASJC Scopus subject areas

  • Biomedical Engineering
  • Medicine(all)

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