Upper limb complex movements decoding from pre-movement EEG signals using wavelet common spatial patterns

2020 ◽  
Vol 183 ◽  
pp. 105076 ◽  
Author(s):  
Mahdieh Mohseni ◽  
Vahid Shalchyan ◽  
Mads Jochumsen ◽  
Imran Khan Niazi
Keyword(s):  
Spatial Patterns ◽  
Upper Limb ◽  
Eeg Signals ◽  
Common Spatial Patterns

scholarly journals Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1385 ◽  
Author(s):  
Shih-Cheng Liao ◽  
Chien-Te Wu ◽  
Hao-Chuan Huang ◽  
Wei-Teng Cheng ◽  
Yi-Hung Liu
Keyword(s):  
Major Depression ◽  
Spatial Patterns ◽  
Filter Bank ◽  
Eeg Signals ◽  
Common Spatial Patterns ◽  
Depression Detection

scholarly journals Regularized common spatial patterns with subject-to-subject transfer of EEG signals

2016 ◽  
Vol 11 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Minmin Cheng ◽  
Zuhong Lu ◽  
Haixian Wang
Keyword(s):  
Spatial Patterns ◽  
Eeg Signals ◽  
Common Spatial Patterns

Discrimination of Walking and Standing from Entropy of EEG Signals and Common Spatial Patterns

2020 ◽  
Author(s):  
Stefano Tortora ◽  
Fiorenzo Artoni ◽  
Luca Tonin ◽  
Carmelo Chisari ◽  
Emanuele Menegatti ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Eeg Signals ◽  
Common Spatial Patterns

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.

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