Neural Comput - A multiscale correlation of wavelet coefficients approach to spike detection.

Tópicos

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Resumo

Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.

Resumo Limpo

extracellular chronic record use import evid neuroscientif studi unveil fundament neural network mechan brain spike detect first step analysi record neural waveform deciph use inform provid use signal brainmachin interfac applic process spike detect extract action potenti record often compound nois differ sourc studi propos new detect algorithm leverag techniqu waveletbas imag edg detect util correl wavelet coeffici differ sampl scale creat robust spike detector algorithm one tune paramet potenti reduc subject detect result artifici benchmark data set real neural record use evalu detect perform propos algorithm compar detect algorithm propos method compar better detect perform letter also demonstr potenti realtim implement

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