The impressive increase in the understanding of some basic processing in the human brain has recently led to the formulation of efficient computational methods, which when applied in the design of better signal processing tools, provides a deeper and clearer view to study the functioning of the human brain. The recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic and magnetoencephalographic recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. Extensions of the basic ICA methodology have also been employed to reveal otherwise hidden information. This paper reviews our recent results in this field.