Motor imagery classification from human EEG signatures

Brain-computer interface (BCI) systems translate the imagination in the human brain to an action in real world by means of machines. These systems can prove to be a blessing for severely impaired patients in terms of BCI prosthetics. This paper proposes a motor imagery classification system based upon Wavelet Packet Decomposition (WPD) and Support Vector Machine (SVM). The wavelet packet transform is used for both selection of sensory motor frequency band and feature extraction. The publically available BCI competition-IV data set-I has been used to evaluate the performance of the proposed scheme. The obtained classification results outperform the previously reported results of the technique Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for subjects 'a' and 'b', where 'a' and 'b' refers to two subjects from BCI competition data set-IV.

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