A transform-based feature extraction approach for motor imagery tasks classification
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Date
2015-10-16
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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 T
2
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%.
Description
Keywords
Brain-computer interface, Channel selection, Feature extraction, Linear prediction, Orthogonal transform
Citation
Baali, H., Khorshidtalab, A., Mesbah, M., & Salami, M. J. (2015). A transform-based feature extraction approach for motor imagery tasks classification. IEEE journal of translational engineering in health and medicine, 3, 1-8.