Biomedical signal processing aims at extracting significant information from physiological signals recorded from the human body. The various biomedical signals such as electroencephalography (EEG), electrocardiography (ECG), magnetoencephalography (MEG) etc. can be analyzed by using both independent component analysis (ICA) and principal component analysis. The main objective of this study is to assess the cognitive status of the individual using independent component analysis (ICA). The methodology of this study is to compare the different ICA algorithms and identify better algorithm for EEG signal processing; extract the feature parameter from the EEG signal and identify the cognitive status of subjects using a feature extracted parameter. Thus, finally the extracted parameter such as alpha, theta and beta band values is used to analyze the cognitive status of the individual. Our study concluded that wavelet packet decomposition of EEG showing increased beta activity and decrease in alpha activity with increase in theta activity during task performance is a good approach to assess mental fatigue and alertness level.