Transmission of biomedical signals through communication channels is being used increasingly in clinical practice. This technique requires dealing with large volumes of information, and the electroencephalographic (EEG) signal is an example of this situation. In the EEG, various channels are recorded during several hours, resulting in a great demand of storage capacity or channel bandwidth. This situation demands the use of efficient data compression systems. The objective of this work was to develop an efficient algorithm for EEG lossy compression. In this algorithm, the EEG signal is segmented and then decomposed through Wavelet Packets (WP). The WP decomposition coefficients are thresholded and those having absolute values below the threshold are deleted. The remaining coefficients are appropriately quantized and coded using a run-length coding scheme. The compressed EEG signal can be recovered by an inverse process. Extensive experimental tests were made by applying the algorithm to EEG records and measuring the compression rate (CR) and the distortion in signal segments. The WP transform showed a high robustness, allowing a reasonably low distortion after a compression-decompression process, for CR typically in the range 5-8. The algorithm has a relatively low computational cost, making it appropriate for practical applications.