Phase Synchronization Features and Common Spatial Patterns for the Classification of Motor Imagery Data

2012 ◽  
Author(s):  
Owen Falzon ◽  
Kenneth P. Camilleri
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Phase Synchronization ◽  
Common Spatial Patterns ◽  
Imagery Data

scholarly journals Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Youngjoo Kim ◽  
Jiwoo Ryu ◽  
Ko Keun Kim ◽  
Clive C. Took ◽  
Danilo P. Mandic ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Supplementary Information ◽  
Eeg Signals ◽  
Common Spatial Patterns ◽  
Right Hand ◽  
Power Difference ◽  
Left And Right ◽  
Beta Rhythms

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.

Classification of Four-Class Motor-Imagery Data for Brain-Computer Interfaces

2021 ◽  
pp. 763-778
Author(s):  
Luu Ngan Thanh ◽  
Duong Anh Hoang Lan ◽  
Nguyen Dung Xuan ◽  
Dang Khiet Thi Thu ◽  
Pham Chau Nu Ngoc ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Imagery Data

Fuzzy system with tabu search learning for classification of motor imagery data

2015 ◽  
Vol 20 ◽  
pp. 61-70 ◽  
Author(s):  
Thanh Nguyen ◽  
Abbas Khosravi ◽  
Douglas Creighton ◽  
Saeid Nahavandi
Keyword(s):  
Tabu Search ◽  
Motor Imagery ◽  
Fuzzy System ◽  
Imagery Data

scholarly journals Single-trial connectivity estimation for classification of motor imagery data

2013 ◽  
Vol 10 (4) ◽  
pp. 046006 ◽  
Author(s):  
Martin Billinger ◽  
Clemens Brunner ◽  
Gernot R Müller-Putz
Keyword(s):  
Motor Imagery ◽  
Single Trial ◽  
Imagery Data
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