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Title: | A transform-based feature extraction approach for motor imagery tasks classification |
Authors: | Baali, Hamza Khorshidtalab, Aida Mesbah, Mostefa Salami, Momoh-Jimoh E. |
Keywords: | Brain-computer interface Channel selection Feature extraction Linear prediction Orthogonal transform |
Issue Date: | 16-Oct-2015 |
Publisher: | IEEE |
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. |
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%. |
URI: | 10.1109/JTEHM.2015.2485261 http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/460 |
Appears in Collections: | Research Articles |
Files in This Item:
File | Description | Size | Format | |
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A transform-based feature extraction approach for motor imagery tasks classification.pdf | Article full-text | 2.73 MB | Adobe PDF | View/Open |
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