Performance analysis of hybrid lossy/lossless compression techniques for EEG data

摘要

Long recording time, large number of electrodes, and a high sampling rate together produce a great data size of Electroencephalography (EEG). Therefore, more bandwidth and space are required for efficient data transmission and storing. So, EEG data compression is a very important problem in order to transmit EEG data efficiently with fewer bandwidth and storing it in a less space. In this paper, We use the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) which are lossy compression methods in order to convert the randomness EEG data to high redundancy data. Therefore, adding a lossless compression algorithm after the lossy compression is a good idea to get a high compression ratio without any distortion in the signal. Here, we use Run Length Encoding (RLE) and Arithmetic Encoding which are lossless compression methods. Total times for compression and reconstruction (T), Compression Ratio (CR), Root Mean Square Error (RMSE), and Structural Similarity Index (SSIM) are evaluated in order to check the effectiveness of the proposed system.