Today efficient brain signal recognition is limited to find different brain diseases; using hardware like Electroencephalogram (EEG), Magnetoencephalograms (MEG), and Functional MRI (fMRI). Alternatively, abnormal brain waves have shown to be associated with particular brain disorders (e.g., Alzheimer’s disease and epilepsy). But the problem with this approach is that there are not many algorithms that could efficiently extract signals from a brain to find Alzheimer’s disease. In this research paper, we suggest the design of new algorithm which could do this job of translating brain signals to digital text data for Alzheimer’s disease. We propose a new approach using which the problem of recognizing brain signals for Alzheimer’s disease can be solved. We also provide the implementation details of this software. For the implementation of our idea, we propose a new intelligent technique Architecture. The technique adopted by the EEG signals used in brain for Alzheimer’s disease, guided and stimulated us to design this research work. Details regarding the automatic preprocessing and decoding interpretation that would be essential and the shortcomings are also included in the research work. © 2015, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.