Brain Computer Interface (BCI) is one of the most dynamic fields in the world of Artificial Intelligence. It involves a direct neural interface between the subject and the machine, and the information is extracted from the brain in the form of waves. This raw brain-wave needs to be classified, de-noised, translated and suitably stored. These steps involve the use of a number of techniques like, the de-noising involves the use of Discrete Wavelet Transform (DWT) [1] or Recurrent Quantum Neural Network (RQNN) [2]. The translation involves the combined use of Fast Fourier Transform and Feed Forward Backpropogation algorithm [1][3]. After translation, the data is stored in a Parse Tree database for faster access [4].This paper proposes a hybrid methodology by bringing together various techniques in order to perform the task of saving the extracted brain-waves in a human-understandable form.