Many brain disorders are very critical and need to be detected at an early stage. This demands the need to create a reliable low-cost, portable system that can acquire the signals and then process and analyze that data. Through this project, the authors aim to address this problem by creating a pre-processing system for an EEG data acquisition system which acquires EEG signals then processes the data to remove noise, and analyze the signal to provide valuable insights. The device is built with several high-pass, low-pass, and notch filters to condition the signal and instrumentation amplifiers for further amplification. An overall amplification of \(90{\times }\) in the first stage and \(450{\times }\) in the last stage is achieved. With the hardware in place, work has been done to integrate the data acquisition system with RLS adaptive filtering algorithms to further enhance the EEG signals by removing EOG artifacts. Later, the signal is analyzed using wavelet transformation and separated into different band frequencies. The proposed post processing system is set to analyze the acquired data for critical brain disorders like depression detection and autism severity indication by using novel approaches.