Efficient EEG Signal Compression Algorithm with Long Length Improved Adaptive Arithmetic Coding and Advanced Division and Encoding Techniques

Electroencephalogram signals reveals the activity of brain and is an important medial signal. In this paper, an efficient compression algorithm for the electroencephalogram signal is proposed. In addition to the improved adaptive arithmetic coding scheme we developed recently, the proposed algorithm also adopts the novel techniques of singular point segmentation, principal coefficient selection, the zero-and-integer approaching scheme, adaptive zero-based run-length coding, and the coding scheme with long-length coding capability. The proposed compression algorithm tested in the electroencephalogram signal database and the results show that the reconstruction error of the proposed algorithm is less than 50% of that of state-of-the-art methods when the compression ratio is 13.66.

I. Introduction

This paper is to develop an efficient architecture for electroencephalogram (EEG) signal compression. The EEG signal [1] is a collection of electrical impulses from the human body. Because the EEG signal usually has a long length, sampled at a high sampling rate, and varies with time rapidly, how to develop an efficient compression algorithm to save the storage space is an important issue for EEG signal processing.