Bayesian Learning Algorithm for Compressive Sensing of Non-Sparse (EEG) Signals
ثبت نشده
چکیده
Compressive Sensing (CS) is an emerging compression technique that takes advantage of a signal’s sparsity to sample and compress this signal at the same time. Its many advantages as well as its satisfactory compression ratios (CR) makes it a very desirable technique in telemonitoring where the bandwidth available is very small and needs to be efficiently used. In the case of electroencephalogram (EEG) signals, the data collected is not sparse and hence CS alone cannot be applied to it. For this reason, a Bayesian learning algorithm (BLA) is proposed in this paper to help with the recovery of the signals at the receiver. This recovery method was shown to outperform the state of the art CS recovery algorithm SPGL1.
منابع مشابه
Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies
This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been shown to yield good results. This method e...
متن کاملSparse Bayesian Learning in Compressive Sensing
Traditional Compressive Sensing (CS) recovery techniques resorts a dictionary matrix to recover a signal. The success of recovery heavily relies on finding a dictionary matrix in which the signal representation is sparse. Achieving a sparse representation does not only depend on the dictionary matrix, but also depends on the data. It is a challenging issue to find an optimal dictionary to recov...
متن کاملBayesian Multi-Task Compressive Sensing with Dirichlet Process Priors
Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. Specifically, if the m-dimensional signal u is sparse in an orthonormal basis represented by the m × m matrix Ψ, then one may infer u based on n m projection measurements. If u = Ψθ, where θ are the sparse coefficients in basis Ψ, the...
متن کاملBayesian compressive sensing for cluster structured sparse signals
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called modelbased compressive sensing, such as clustered structure and tree structure on wavelet coeff...
متن کاملCompressive Sensing for Cluster Structured Sparse Signals: Variational Bayes Approach
Compressive Sensing (CS) provides a new paradigm of sub-Nyquist sampling which can be considered as an alternative to Nyquist sampling theorem. In particular, providing that signals are with sparse representations in some known space (or domain), information can be perfectly preserved even with small amount of measurements captured by random projections. Besides sparsity prior of signals, the i...
متن کامل