Songs induced mood recognition system using EEG signals

BACKGROUND: Brain computer interfacing is a system that acquires and analyzes neural signals to create a communication channel directly between the brain and the computer. The EEG records the electrical fields generated by the nerve cells. With the help of Fourier Transformation the EEG signals are classified into four different frequency bands. PURPOSE: The main purpose of the present paper is to report results related to classification of EEG signals of different people subjected to different conditions. METHODS: The experiment has been done on 10 subjects having activities related to hearing music chosen from categories of patriotic, happy, romantic and sad songs along with relaxation activity. 19 electrodes have been used under (10–20) International Standard. The δ, θ α and β components of EEG signals to these activities have been determined. Different statistical methods including linear discriminate analysis have been tested for classification. RESULTS: Result of the Linear Discriminant Analysis (LDA) made four groups of all modes (Relaxation, Happy, Sad, Patriotic and Romantic Song) labeled group1, Group2, Group3 and Group4 of all ten electrodes for Delta, Theta, alpha and Beta frequencies. CONCLUSION: The study may be used for the development of activities induced mood recognition (AIMR) system from the EEG signal.