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

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 374; no. 2065; p. 20150199
• Main Authors: Hemakom, Apit, Goverdovsky, Valentin, Looney, David, Mandic, Danilo P.
• Format: Journal Article
• Language: English
• Published: England The Royal Society Publishing 13.04.2016
• Subjects: Adaptive Projection; Algorithms; Biomedical Engineering; Brain-Computer Interface; Brain-Computer Interfaces; Electroencephalography; Humans; Intrinsic Multiscale Analysis; Multivariate Empirical Mode Decomposition; Signal Processing, Computer-Assisted; brain–computer interface; adaptive projection; multivariate empirical mode decomposition; intrinsic multiscale analysis
• ISSN: 1364-503X; 1471-2962
• DOI: 10.1098/rsta.2015.0199

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.