A Novel Weighted LBG Algorithm for Neural Spike Compression
Sudhir Rao, António R. C. Paiva, José Carlos Príncipe · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
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.