  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.
