Algorithms and architectures for low power spike detection and alignment

Abstract

We introduce algorithms and architectures for automatic spike detection and alignment that are designed for low power. Some of the algorithms are based on principal component analysis (PCA). Others employ a novel integral transform analysis and achieve 99% of the precision of a PCA detector, while requiring only 0.05% of the computational complexity. The algorithms execute autonomously, but require off-line training and setting of computational parameters. We employ pre-recorded neuronal signals to evaluate the accuracy of the proposed algorithms and architectures: the recorded data are processed by a standard PCA spike detection and alignment software algorithm, as well as by the several hardware algorithms, and the outcomes are compared.

On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes

“…In fact, several works reported the feasibility of similar algorithms running on small low power devices, such as stationary wavelet-based denoising algorithm on programmable DSP and FPGA (Montani et al, 2003), and algorithms for spike detection, alignment and spike sorting on VLSI architectures (Zumsteg et al, 2005;Zviagintsev et al, 2006). Signals recorded from chronic setups are dynamic in that the responses to repeated stimuli or motor commands change in time.…”

Detection of neuronal spikes using an adaptive threshold based on the max–min spread sorting method

“…The above steps are repeated based on the spike-removed signal until T 1 > X 0.9987 . As the demand of implementing spike manipulation by embedded, low-power chips or systems increases (Harrison, 2003;Zumsteg et al, 2005;Farshchi et al, 2006;Zviagintsev et al, 2006;Perelman and Ginosar, 2007;Sodagar et al, 2007), simple, efficient spike detection methods are needed. In 2003, Harrison developed an adaptive threshold detector which can be implemented by an analog integrated circuit.…”

Neural Data Compression with Wavelet Transform: A Vocabulary Based Approach

“…With the advances in bio-MEMS and microelectronics, interpreting and engineering the activity of the central nervous system are likely to make dramatic progress using miniaturized implantable biomedical microsystems [1][2][3][4][5][6][7][8][9][10][11][12][13]. These implants offer new possibilities in terms of medical diagnosis and therapy [2,[12][13].…”

“…Since then, numerous efforts [5][6][7][8][9][10][11][12][13][14] have been pursued in this direction. Currently, it is possible to precisely measure the activity of very few neurons (e.g.…”

“…In an unsupervised spike detection and classification, an adaptive thresholding scheme is often used for detection, while principal component analysis [11] and fuzzy c-means (FCM) clustering are commonly used for spike classification. Another detection technique based on the Teager energy Operator (TEO) is reported to show relatively good performance at low signal to noise ratios (SNRs) [8].…”