Classification of Left and Right Hand Motor Imagery Tasks Based on EEG Frequency Component Selection

2008 ◽  
Author(s):  
Xiaomei Pei ◽  
Chongxun Zheng
Keyword(s):  
Motor Imagery ◽  
Frequency Component ◽  
Component Selection ◽  
Right Hand ◽  
Left And Right

scholarly journals Mental rotation of congenitally absent hands

2007 ◽  
Vol 14 (1) ◽  
pp. 81-89 ◽  
Author(s):  
MARION FUNK ◽  
PETER BRUGGER
Keyword(s):  
Mental Rotation ◽  
Motor Imagery ◽  
Visual Recognition ◽  
Reaction Times ◽  
Right Hand ◽  
Left And Right ◽  
The One ◽  
The Individual ◽  
Phantom Limbs

We compared motor imagery performance of normally limbed individuals with that of individuals with one or both hands missing since birth (i.e., hand amelia). To this aim, 14 unilaterally and 2 bilaterally amelic participants performed a task requiring the classification of hands depicted in different degrees of rotation as either a left or a right hand. On the same task, 24 normally limbed participants recapitulated previously reported effects; that is, that the hand motor dominance and, more generally, a lifelong use of hands are important determinants of left–right decisions. Unilaterally amelic participants responded slower to hands corresponding to their absent, compared with their existing, hand. Moreover, left and right hand amelic participants showed prolonged reaction times to hands (whether left or right) depicted in unnatural orientations compared with natural orientations. Among the bilateral amelics, the individual with phantom sensations, but not the one without, showed similar differentiation. These findings demonstrate that the visual recognition of a hand never physically developed is prolonged, but still modulated by different rotation angles. They are further compatible with the view that phantom limbs in hand amelia may constrain motor imagery as much as do amputation phantoms. (JINS, 2008,14, 81–89.)

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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