Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 116 - Number 10
Year of Publication: 2015
Authors:
B. Senthil Kumar
S. Santhosh Baboo
10.5120/20370-2577

B.senthil Kumar and S.santhosh Baboo. Article: Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal. International Journal of Computer Applications 116(10):6-11, April 2015. Full text available. BibTeX

@article{key:article,
	author = {B.senthil Kumar and S.santhosh Baboo},
	title = {Article: Linking and Familiarity Rating Method Classifies the Music, Video Assessment Responses of EEG-Signal},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {116},
	number = {10},
	pages = {6-11},
	month = {April},
	note = {Full text available}
}

Abstract

The most renowned strategy utilized for perusing mind movement is electroencephalography (EEG). Electroencephalography is the neurophysiologic estimation of the electrical action of the cerebrum by recording from anodes put on the scalp, or in the exceptional cases on the cortex. The ensuing follows are known as an electroencephalogram (EEG) and speak to alleged brainwaves. This system is picking up prevalence as it is a non-intrusive interface and is giving a methodology to controlling machines through contemplations. The proposed linking and familiarity rating method classifies the music, video assessment responses of EEG-Signal. The metrics namely true positive, true negative, false positive, false negative, sensitivity, specificity and classification accuracy are chosen for evaluating the performance of the proposed classifier. The simulation result shows that the proposed classifier achieves 95. 4 % accuracy which is better than other methods.

References

  • Adams, Bahr, Moreno, "Brain Computer Interfaces: Psychology and Pragmatic Perspectives for the Future", AISB 2008 Convention, Aberdeen, Scotland.
  • Auger, Flandrin, Goncalves, Lemoine, Time-Frequency Toolbox Tutorial. Centre Nat'l de la Recherche Scientifique (CNRS)/Rice Univ. , 1996.
  • Bachorik, Bangert, Loui, "Emotion in Motion: Investigating the Time-Course of Emotional Judgments of Musical Stimuli," Music Perception, vol. 26, no. 4, pp. 355-364, 2009.
  • Boashash, "Theory of Quadratic TFDs," Time Frequency Signal Analysis and Processing: A Comprehensive Reference, B. Boashash, ed. , pp. 59-82, Elsevier, 2003.
  • Borstein, "Exposure and Affect: Overview and Meta-Analysis of Research 1968-1987," Psychological Bull. , vol. 106, no. 2, pp. 265-289, 1989.
  • Cohen, "Time-Frequency Distributions—A Review," Proc. IEEE, vol. 77, no. 7, pp. 941-981, July 1989.
  • Hadjidimitriou and Hadjileontiadis, "Towards an EEGBased Recognition of Music Liking Using Time-Frequency Analysis," IEEE Trans. Biomedical Eng. , vol. 59, no. 12, pp. 3498-3510, Dec. 2012.
  • Hadjidimitriou, Hadjileontiadis,"EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings", IEEE Transactions On Affective Computing, Vol. 4, No. 2, April-June 2013
  • Huang, Shen, Long, Wu, Shih, Zheng, Yen, Tung, Liu, "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis," Proc. Royal Soc. London. Series A: Math. , Physical and Eng. Sciences, vol. 454, no. 1971, pp. 903-995, 1998.
  • Hunter Schellenberg, "Interactive Effects of Personality and Frequency of Exposure on Liking for Music," Personality Individual Differences, vol. 50, no. 2, pp. 175-179, 2010.
  • Niedermeyer, Lopes da Silva, "Electroencephalography: Basic Principles, Clinical Applications and Related Fields", Williams and Wilkins, 1998.
  • Pfurtscheller, Lopes da Silva, "Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles,"Clinical Neurophysiology, vol. 110, pp. 1842-1857, 1999.