Finding stationary subspaces in multivariate time series.
Explore this paper's citation graph
Summary
This Letter proposes a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part and succeeds in finding stationary components that lead to a significantly improved prediction accuracy and meaningful topographic maps which contribute to a better understanding of the underlyingnonstationary brain processes.
- Type
- article
- Published
- 2009-11-20
- Cited by
- 256
- References
- 0
- OpenAlex
- https://openalex.org/W1991410152
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:21976484
Keywords
Multivariate statistics, Series (stratigraphy), Linear subspace, Computer science, Subspace topology
References
No references recorded for this paper.
Cited by
- Feature-based transfer learning with real-world applications
- Separation of stationary and non-stationary sources with a generalized eigenvalue problem
- Spatial filter adaptation based on the divergence framework for motor imagery EEG classification
- Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing
- Learning from weakly representative data and applications in spectral image analysis
- Domain Adaptation via Transfer Component Analysis
- Joint optimization for discriminative, compact and robust Brain-Computer Interfacing
- Locating and extracting acoustic and neural signals
- Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
- Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond
- Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity
- Algebraic Geometric Comparison of Probability Distributions
- Importance-weighted covariance estimation for robust common spatial pattern
- Multimodal imaging, non-stationarity and BCI
- Co-adaptive calibration to improve BCI efficiency
- Robust Common Spatial Filters with a Maxmin Approach
- The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
- Blind separation of heart sounds
- Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter
- Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight]