Proceedings ArticleDOI
Training-free compressed sensing for wireless neural recording
Biao Sun,Yuming Ni,Wenfeng Zhao +2 more
- pp 18-21
TLDR
Experimental results reveal that the proposed approach outperforms the state-of-the-art CS methods for wireless neural recording and reduces the need for previously acquired data and computational complexity.Abstract:
Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and Compressed Sensing (CS) has successfully demonstrated its potential in this field. However, the conventional CS approaches rely on data-dependent and computationally intensive dictionary learning processes to find out the sparse representation of neural signals, and dictionary re-training is inevitable during real experiments. This paper proposes a training-free CS approach for wireless neural recording. By adopting the analysis model to enforce the signal sparsity and constructing a multi-order difference matrix as the analysis operator, it avoids the dictionary learning procedure and reduces the need for previously acquired data and computational complexity. In addition, a group weighted analysis 11-minimization method is developed to recover the neural signals. Experimental results reveal that the proposed approach outperforms the state-of-the-art CS methods for wireless neural recording.read more
Citations
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Journal ArticleDOI
On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.
TL;DR: It is demonstrated that the proposed CS encoders lead to comparable recovery performance and efficient VLSI architecture designs are proposed for QCAC-CS and $(1,s)$-SRBM encoder designs with reduced area and total power consumption.
Journal ArticleDOI
Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach
TL;DR: A binarized autoencoder scheme for CS is presented, in which a binary sensing matrix and a noniterative recovery solver are jointly optimized, which outperforms the state-of-the-art CS-based methods both in terms of recovery quality and computation time.
Journal ArticleDOI
Modeling the Active Neuron Separation in the Compressed Sensing and Finite Rate of Innovation Framework
TL;DR: A model for the problem of separating active neurons from their superposition at an array of electrodes in a neural recording setting is presented and a total of four solutions for the extraction of the pulse shape, number, and activity patterns of active neurons are presented.
Journal ArticleDOI
A triangular hashing learning approach for olfactory EEG signal recognition
TL;DR: In this article , a novel triangular hashing (TH) approach is proposed for EEG signal recognition, which consists of a triangular feature construction and a hash inspired coding idea, which makes effective use of the feature differences between EEG electrodes.
Journal ArticleDOI
A Training-Free One-Bit Compressed Sensing Framework for Wireless Neural Recording
TL;DR: Experimental results reveal that the proposed approach not only drastically reduces the transmission bits but also outperforms the state-of-the-art CS-based methods in terms of both recovery accuracy and noise robustness.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program.
Posted Content
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In this article, it was shown that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law, then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements.
Journal ArticleDOI
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
TL;DR: The aim of this paper is to introduce a few key notions and applications connected to sparsity, targeting newcomers interested in either the mathematical aspects of this area or its applications.
Journal ArticleDOI
Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
TL;DR: A new method for detecting and sorting spikes from multiunit recordings that combines the wave let transform with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions is introduced.