Temporal Modeling of EEG Signals using Block Sparse Variational Bayes Framework
TL;DR: 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.
"Temporal Modeling of EEG Signals us..." refers background in this paper
...(4) Conjugate priors ensures that the chosen prior and the posterior distribution are of same functional form, resulting in tractable closed form solution ....
"Temporal Modeling of EEG Signals us..." refers background or methods in this paper
...In VB, we seek for the distribution q(θr ;θd ) that approximates the posterior p(θr |y;θd ) which can be found out by minimizing KL divergence between them [5, 15]....
...Using the principles of Automatic Relevance Determination (ARD) , it can be seen that as the estimates of αi → ∞, the whole block xi can be pruned out and hence block sparsity can be induced....
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