Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for Detecting Upper Limb Movement in EEG-EMG Hybrid Brain Computer Interface

2021 ◽  
Author(s):  
Yi Zhang ◽  
Lifu Zhang ◽  
Guan Wang ◽  
Wenyi Lyu ◽  
Yu Ran ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Upper Limb ◽  
Brain Computer Interface ◽  
Limb Movement ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

scholarly journals Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150199 ◽  
Author(s):  
Apit Hemakom ◽  
Valentin Goverdovsky ◽  
David Looney ◽  
Danilo P. Mandic
Keyword(s):  
Empirical Mode Decomposition ◽  
Information Transfer ◽  
Multiscale Analysis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Analysis Framework ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Information Transfer Rate ◽  
Multichannel Data

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain–computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.

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