Proceedings ArticleDOI
Temporal Modeling of EEG Signals using Block Sparse Variational Bayes Framework
TLDR
Results show that the proposed temporal model is highly useful in processing SSVEP-EEG signals irrespective of the recognition algorithms used.Abstract:
Compressed Sensing (CS) has emerged as an alternate method to acquire high dimensional signals effectively by exploiting the sparsity assumption. However, owing to non-sparse and non-stationary nature, it is extremely difficult to process Electroencephalograph (EEG) signals using CS paradigm. The success of Bayesian algorithms in recovering non-sparse signals has triggered the research in CS based models for neurophysiological signal processing. In this paper, we address the problem of Temporal Modeling of EEG Signals using Block Sparse Variational Bayes (SVB) Framework. Temporal correlation of EEG signals is modeled blockwise using normal variance scale mixtures parameterized via some random and deterministic parameters. Variational inference is exploited to infer the random parameters and Expectation Maximization (EM) is used to obtain the estimate of deterministic parameters. To validate the framework, we present experimental results for benchmark State Visual Evoked Potential (SSVEP) dataset with 40-target Brain-Computer Interface (BCI) speller using two frequency recognition algorithms viz. Canonical Correlation Analysis (CCA) and L1-regularized Multiway CCA. Results show that the proposed temporal model is highly useful in processing SSVEP-EEG signals irrespective of the recognition algorithms used.read more
Citations
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Book ChapterDOI
Some Comments on Variational Bayes Block Sparse Modeling with Correlated Entries
TL;DR: The effect of the threshold to prune out variance parameters of algorithms corresponding to several choices of marginals, viz. multivariate Jeffery prior, multivariate Laplace distribution and multivariate Student’s t distribution is discussed.
References
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A Mathematical Introduction to Compressive Sensing
Simon Foucart,Holger Rauhut +1 more
TL;DR: A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build and serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject.
Journal ArticleDOI
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
TL;DR: The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem.
Journal ArticleDOI
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
TL;DR: A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI) that were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.
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