A Novel Weighted LBG Algorithm for Neural Spike Compression

Author

Rao, Sudhir ; Paiva, António R C ; Príncipe, José C.

Author_Institution

Florida Univ., Gainesville

fYear

2007

fDate

12-17 Aug. 2007

Firstpage

1883

Lastpage

1887

Abstract

In this paper, we present a weighted Linde-Buzo-Gray algorithm (WLBG) as a powerful and efficient technique for compressing neural spike data. We compare this technique with the recently proposed self-organizing map with dynamic learning (SOM-DL) and the traditional SOM. A significant achievement of WLBG over SOM-DL is a 15 dB increase in the SNR of the spike data apart from having a compression ratio of 150 : 1. Being simple and extremely fast, this algorithm allows real-time implementation on DSP chips opening new opportunities in BMI applications.

Keywords

bioelectric phenomena; brain; data compression; digital signal processing chips; handicapped aids; medical signal processing; neurophysiology; real-time systems; self-organising feature maps; DSP chip; brain machine interface; dynamic learning self-organizing map; motor-impaired patient assistance; neural spike data compression; paralysed patient assistance; real-time implementation; weighted Linde-Buzo-Gray algorithm; Bandwidth; Communication system control; Distortion measurement; Humans; Neurons; Prosthetics; Self organizing feature maps; Signal processing; Signal processing algorithms; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Conference_Location

Orlando, FL

ISSN

1098-7576

Print_ISBN

978-1-4244-1379-9

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2007.4371245

Filename

4371245