Delorme, A., Plamer, J., Oostenveld, R., Onton, J. and Makeig, S. (2007) Comparing results of algorithms implementing blind source separation of EEG data. Swartz Foundation and NIH Grant.

has been cited by the following article:

• TITLE: Processing obstructive sleep apnea syndrome (OSAS) data

  AUTHORS: Ren Sin Tung, Wai Yie Leong

  KEYWORDS: EEG; Obstructive Sleep Apnea Syndrome (OSAS); Independent Component Analysis (ICA); Wavelets Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.2, February 27, 2013

  ABSTRACT: 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.