In this study, the EEG signals were processed. Thirteen ICA algorithms were tested to verify the performance efficiency. The EEG signals were recorder using 10/20 international system, based on a 20 minute sleep recording of a severe Obstructive Sleep Apnea Syndrome (OSAS) during NREM and REM sleep. Seven channels were used to record the EEG signals which are sampled at 100 Hz. The performance analysis of the algorithms were investigated to eliminate the loss of the informative EEG signal during the data processing. The denoising results were magnified with the purpose of evaluating the robustness of the denoising algorithms. From the result we obtained, we are able to understand the denoising algorithm is more suitable to process the EEG signal with lower amplitude.
 Romo-Vazquez, R., Ranta, R., Luis-Dorr, V. and Maquin, D. (2007) Ocular artifacts removal in scalp EEG: Combining ICA and wavelet denoising. The 5th International Conference on Physics in Signal and Image Processing, Mulhouse, 31 January-2 February 2007.
 Romo-Vazquez, R., Ranta, R., Luis-Dorr, V. and Maquin, D. (2007) EEG ocular artefacts and noise removal. The 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, Lyon, 22 26 August 2007, 5445-5448.
 Ullah, K., Khan, M.A.U. and Kundi, R.U. (2010) What ICA provides for ECG signal extraction from contaminated ECG observations without using differential amplifiers. International Conference on Information and Emerging Technologies, Karachi, 14-16 June 2010, 1-5.
 Gharieb, R.R. and Cichocki, A. (2003) Second order statistic based blind source separation using a bank of sub band filters. Digital Signal Processing, 13, 252-274. doi:10.1016/S1051-2004(02)00034-9
 Cichocki, A., Amari, S., Siwek, K., Tanaka, T., et al. ICALAB Toolboxes. http://www.bsp.brain.riken.jp/ICALAB.2007