Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Neural Information Processing LNCS, 2015, 9490 pp. 1 - 9
Issue Date:
2015-01-01
Full metadata record
Files in This Item:
Filename Description Size
415.pdfAccepted Manuscript version3.18 MB
Adobe PDF
© Springer International Publishing Switzerland 2015. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35 %, the LP-SVD based method outperformed the other two by large margins (+25 % compared to DCT and +6 % compared to AAR-based methods).
Please use this identifier to cite or link to this item: