Improved method for analyzing electrical data obtained from EEG for better diagnosis of brain related disorders

Interpretation of data obtained from electroencephalogram (EEG) has been commonly used for studying the condition of the brain and to diagnose any abnormalities. However, it is a common occurrence that different characteristic waves from EEG may overlap each other which may cause inaccuracies leading to wrong interpretation and hence incorrect diagnosis. In this paper, we present a modified approach to analyze the EEG signals differently. Firstly, the data is normalized, and then divided into three different ranges of equal intervals. This is done for each characteristic wave of EEG data. We have applied the proposed approach to brainwave datasets taken from Kaggle and IEEE dataport. The proposed method helps in estimating the current condition of the brain with higher accuracy, due to quantified contribution of different waves. The presented approach is expected to eliminate any errors, which may exist presently in the diagnosis of brain-related diseases and disorders. The discussed approach presented in this paper is dependent on data analysis, and it does not depends on the way of conducting the EEG tests. Hence, it is extendable to other disorders of the human body. The proposed approach finds many applications to improve the accuracy of brain related disorders by repudiating data overlap by 0.01% improvements.