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

Hamza Baali, Aida Khorshidtalab, Mostefa Mesbah, Momoh J. E. Salami
2015 IEEE Journal of Translational Engineering in Health and Medicine  
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
more » ... ssified 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%. INDEX TERMS Brain-computer interface, channel selection, feature extraction, linear prediction, orthogonal transform.
doi:10.1109/jtehm.2015.2485261 pmid:27170898 pmcid:PMC4861551 fatcat:jv4wsh7acfcszbdjhleftc6tzu