2018/04/08 by Madyan Alsenwi, Alsenwi, Madyan, Tawfik Ismail +3
Computer Science · Engineering · Medicine · Neuroscience · #Advanced Data Compression Techniques #Analog and Mixed-Signal Circuit Design #Blind Source Separation Techniques #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1804.02713
openalex publication_date 2018/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with less bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit. Then, the resulting EEG data are passed to the compression unite. The compression unit composes of a lossy compression algorithm followed by a lossless compression algorithm. The lossy compression algorithm transforms the randomness EEG data into data with high redundancy. Subsequently, A lossless compression algorithm is added to investigate the high redundancy of the resulting data to get high Compression Ratio (CR) without any additional loss. In this paper, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are proposed as a lossy compression algorithm. Furthermore, Arithmetic Encoding and Run Length Encoding (RLE) are proposed as a lossless compression algorithm. We calculate the total compression and reconstruction time (T), Root Mean Square Error (RMSE), and CR in order to evaluate the proposed system. Simulation results show that adding RLE after the DCT algorithm gives the best performance in terms of compression ratio and complexity. Using the DCT as a lossy compression algorithm followed by the RLE as a lossless compression algorithm gives CR=90% at RMSE=0.14 and more than 95% of CR at RMSE=0.2.