Title :
A Low-Power Spike Detection and Alignment Algorithm
Author :
Zviagintsev, Alex ; Perelman, Yevgeny ; Ginosar, Ran
Author_Institution :
VLSI Syst. Res. Center, Israel Inst. of Technol., Haifa
Abstract :
Front-end integrated circuits for signal processing are useful in neuronal recording systems that engage a large number of electrodes. Detecting, alignment, and sorting the spike data at the front-end reduces the data bandwidth and enables wireless communication. Without such data reduction, large data volumes need to be transferred to a host computer and typically heavy cables are required which constrain the patient or test animal. Front-end processing circuits can dissipate only a limited amount of power, due to supply constraints and heat restrictions. Reduced complexity spike detection and alignment algorithm and architecture, based on integral transform, are introduced. They achieve 99% of the precision of a PCA detector, while requiring only 0.05% of the computational complexity
Keywords :
bioelectric potentials; biomedical electrodes; computational complexity; data reduction; medical signal detection; medical signal processing; neurophysiology; principal component analysis; satellite computers; transforms; PCA; computational complexity; data reduction; electrodes; front-end integrated circuits; front-end processing circuits; integral transform; low-power spike detection; neuronal recording systems; signal processing; spike alignment; spike sorting; wireless communication; Animals; Bandwidth; Circuit testing; Communication cables; Computer architecture; Electrodes; Power supplies; Signal processing algorithms; Sorting; Wireless communication;
Conference_Titel :
Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-8710-4
DOI :
10.1109/CNE.2005.1419621