title: Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns.
abstract: Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.
authors: Department of Computer Engineering, Kwangwoon University, 20 Gwangun Rd, Nowon-gu, Seoul 01897, Republic of Korea.
authors: Youngjoo
authors: 0000-0002-9677-163X
authors: Y
authors: Kim
authors: Department of Computer Engineering, Kwangwoon University, 20 Gwangun Rd, Nowon-gu, Seoul 01897, Republic of Korea.
authors: Jiwoo
authors: J
authors: Ryu
authors: LG, 38 Baumoe-ro, Seocho-gu, Seoul 137724, Republic of Korea.
authors: Ko Keun
authors: KK
authors: Kim
authors: Department of Computing, University of Surrey, Guildford, Surrey GU27XH, UK.
authors: Clive C
authors: 0000-0002-1640-1381
authors: CC
authors: Took
authors: Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW72AZ, UK.
authors: Danilo P
authors: DP
authors: Mandic
authors: Department of Computer Engineering, Kwangwoon University, 20 Gwangun Rd, Nowon-gu, Seoul 01897, Republic of Korea.
authors: Cheolsoo
authors: 0000-0001-8042-007X
authors: C
authors: Park
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography; D019054:Evoked Potentials, Motor / Q000502:physiology*; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity