No abstract

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

In this paper a new dictionary and memory based text compression technique is presented called a TwoLevel Dictionary Based Text Compression Scheme. The original words in a text file are transformed into codewords having length 2 and 3 using a dictionary comprising 73680 frequently used words in English language. Among these words most frequently used words use 2 length codewords and the rest use 3 length codewords for better compression. The codewords are chosen in such way that the spaces between words in the original text file can be removed altogether recovering a substantial amount of space. Another unique feature of our compression scheme is that we have recovered unused bit of ASCII character representation from each character to save one byte per 8 ASCII characters. Lastly a back end existing compression algorithm is used to finally compress the file. We have achieved about 75% (compression ratio of 2.01 bits per input character) reduction in size using our new compression strategy with gzip and bzip2.Index Terms -Text compression, Dictionary based compression, Huffman code, word transformation.