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This paper presents a power-efficient VLSI implementation of a feature extraction engine for the applications of real-time spike sorting. Traditional method like principal components analysis (PCA) works in a batch mode by diagonalizing the covariance matrix constructed from the whole bunch of input data, which is computationally prohibitive and does not favor real-time processing. The proposed hardware...
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection...
This paper presents a novel nonparametric clustering algorithm, called energy based evolving mean shift (EMS) clustering. It defines an energy function to characterize the compactness of the underlying data set and proves the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. Although as a general algorithm,...
An emerging class of multi-channel neural recording systems aims to simultaneously monitor the activity of many neurons by miniaturizing and increasing the number of recording channels. Vast volume of data from the recording systems, however, presents a challenge for processing and transmitting wirelessly. An on-chip neural signal processor is needed for filtering uninterested recording samples and...
Successful proof-of-concept laboratory experiments on cortically controlled motor prostheses, brain pacemakers and hippocampal prostheses motivate continued development for neural prosthetic systems. Advances in implantable electrode arrays and miniaturized multichannel recording ICs enable long-duration, wireless and closed-loop experiments on freely moving subjects. To further improve clinically...
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