ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2016
Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Multivariate Multiscale Cosine Similarity Entropy and Its Application to Examine Circularity Properties in Division Algebras.Entropy (Basel, Switzerland) · 2022Article
- Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.Sensors (Basel, Switzerland) · 2021Article
- Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition.Computational intelligence and neuroscience · 2019Article
- Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification.Sensors (Basel, Switzerland) · 2018Article
- Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.Sensors (Basel, Switzerland) · 2018Article
- Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV.Frontiers in physiology · 2017Article
- Adaptive data analysis: theory and applications.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Article
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4 authors.
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Abstract
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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