Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 20, Pages: 1-9
Vivia Mary1*, S. S. Sridhar2 and Mochitha Vijayan2
1Department of Information Technology, SRM University, Kattankulathur Campus, Chennai – 603203, Tamil Nadu, India; [email protected] 2Department of Computer Science, SRM University, Kattankulathur Campus, Chennai – 603203, Tamil Nadu, India; [email protected], [email protected]
*Author for the correspondence:
Department of Information Technology, SRM University, Kattankulathur Campus, Chennai – 603203, Tamil Nadu, India; [email protected]
Objectives: The paper is based on the P300 component of the Visual Evoked Potential. The objective is to collect visual responses of humans and categorize them by age and blood group. Methods/Analysis: The device used to collect data is a NeuroSky MindWave Mobile Headset along with an Android application: eegID. The data is manually compared with other individuals of the same age and blood group and the values showing similar peaks are picked out from the entire set. The data attributes is normalized by using the formula. The normalized values are then grouped according to age and blood group and plotted. Findings: The graphs plotted from the data show that the emotional characteristics of individuals belonging to the same age groups and blood groups are similar. Slight variations were noted among individuals of similar blood groups but different age groups. A similar experiment has been conducted for comparing emotions using audio stimulus. A questionnaire was created which was compared with the values achieved in the results. This showed similarities thus proving that the experiment achieved results matching individuals’ personal choices. The existing and proposed system both used ERP generated by audio and visual stimuli respectively. The existing data can be combined with the data achieved in this paper to generate a more detailed study into the characteristics of individuals categorized by their blood group.
Keywords: Brain Computer Interface, EEG Emotional, P300, Quotient, Visual Evoked Potential
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