A Neoteric Approach Towards Anxiety Quantification by EEG Signal Analysis

Anxiety has become a moiety of day today life for many, an inseparable complement of every endeavor. This serves as a fountain head of several complications and health hazards. Anxiety causes various changes in the body of which those reflected by the Central Nervous System (CNS) are predominant. This paper focuses on the exhaustive analysis of the electroencephalogram (EEG) signal concerning anxiety quantification. The signals are taken from various databases of which the MIT-BIH and Physionet databases are worth mentioning, then four chosen classification algorithms namely, KNN, QDA, LDA and D Tree respectively are applied, the Principle Component Analysis is utilized as the feature classification tool and the spectral characteristics of the sub bands are studied. Lastly the FFT is applied to the raw EEG to perform its frequency domain analysis and anxiety is thus detected in PC using MATLAB as the tool.