You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Discrimination of motor imagery tasks via information flow pattern of brain connectivity

Abstract

BACKGROUND:

The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain.

OBJECTIVE:

This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks.

METHODS:

The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern.

RESULTS:

Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs).

CONCLUSION:

The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).

References

[1] 

Friston K J. Functional and effective connectivity: a review[J]. Brain connectivity. 2011, 1(1): 13-36.

[2] 

Kaminski M J, , Blinowska K J. A new method of the description of the information flow in the brain structures[J]. Biological cybernetics. 1991, 65(2): 203-210.

[3] 

Baccalá L A, , Sameshima K. Partial directed coherence: a new concept in neural structure determination[J]. Biological cybernetics. 2001, 84(6): 463-474.

[4] 

Gourévitch B, , Le Bouquin-Jeannès R, , Faucon G. Linear and nonlinear causality between signals: methods, examples and neurophysiological applications[J]. Biological cybernetics. 2006, 95(4): 349-369.

[5] 

Al-Fahoum A S, , Al-Fraihat A A. Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains[J]. ISRN neuroscience. 2014.

[6] 

Rehman N, , Mandic D P. Multivariate empirical mode decomposition[C]//Proceedings of The Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society, 2009: rspa20090502.

[7] 

Tangermann M, , Müller K R, , Aertsen A, et al. Review of the BCI competition IV[J]. Frontiers in neuroscience. 2012, 6.

[8] 

Ramoser H, , Muller-Gerking J, , Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement[J]. Rehabilitation Engineering, IEEE Transactions on. 2000, 8(4): 441-446.

[9] 

Schlögl A. A comparison of multivariate autoregressive estimators[J]. Signal processing. 2006, 86(9): 2426-2429.

[10] 

Yasumasa TD, , Antonio BL, , Sameshima K. Connectivity inference between neural structures via partial directed coherence[J]. Journal of Applied Statistics. 2007, 34(10): 1259-1273.

[11] 

Gao Q, , Duan X, , Chen H. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality[J]. Neuroimage. 2011, 54(2): 1280-1288.