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A Novel framework of EEG-based user identification by analyzing music-listening behavior

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Abstract

This paper introduces a novel framework for user identification by analyzing neuro-signals. Studies regarding Electroencephalography (EEG) revealed that such bio-signals are sensitive, hard to forge, confidential, and unique which the conventional biometric systems like face, speaker, signature and voice lack. Traditionally, researchers investigated the neuro-signal patterns by asking users to perform various imaginary, visual or calculative tasks. In this work, we have analyzed this neuro-signal pattern using audio as stimuli. The EEG signals are recorded simultaneously while user is listening to music. Four different genres of music are considered as users have their own preference and accordingly they respond with different emotions and interests. The users are also asked to provide music preference which acts as a personal identification mechanism. The framework offers the benefit of uniqueness in neuro-signal pattern even with the same music preference by different users. We used two different classifiers i.e. Hidden Markov Model (HMM) based temporal classifier and Support Vector Machine (SVM) for user identification system. A dataset of 2400 EEG signals while listening to music was collected from 60 users. User identification performance of 97.50 % and 93.83 % have been recorded with HMM and SVM classifiers, respectively. Finally, the performance of the system is also evaluated on various emotional states after showing different emotional videos to users.

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Notes

  1. http://htk.eng.cam.ac.uk/download.shtml

  2. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  3. https://sites.google.com/site/kaurbarjinder/

References

  1. Abdulkader SN, Atia A, Mostafa M-SM (2015) Single trial authentication with mental password writing. In: Human Aspects of Information Security, Privacy, and Trust, pp 3–12

  2. Abo-Zahhad M, Ahmed SM, Abbas SN (2015) A new multi-level approach to EEG based human authentication using eye blinking. Pattern Recogn Lett

  3. Al-Juboori AM, Wu X, Zhao Q (2013) Biometric authentication system based on palm vein. In: International Conference on Computer Sciences and Applications, pp 52–58

  4. Alomari MH, Abubaker A, Turani A, Baniyounes AM, Manasreh A (2014) EEG mouse: A machine learning-based brain computer interface. Int J Adv Comput Sci Appl 5(4):193–198

    Google Scholar 

  5. Alzahrani H, Boult TE (2014) Remote authentication using vaulted fingerprint verification. In: SPIE Defense+ Security, pp 90750K–90750K

  6. Astigarraga A, Arruti A, Muguerza J, Santana R, Martin JI, Sierra B (2016) User adapted motor-imaginary brain-computer interface by means of EEG channel selection based on estimation of distributed algorithms. Math Probl Eng

  7. Azami H, Mohammadi K, Bozorgtabar B (2012) An improved signal segmentation using moving average and savitzky-golay filter. Journal of Signal and Information Processing

  8. Badcock NA, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G (2013) Validation of the emotiv EPOC®; EEG gaming system for measuring research quality auditory ERPs. PeerJ 1:e38

  9. Breitwieser C, Terbu O, Holzinger A, Brunner C, Lindstaedt S, Müller-putz GR (2012) iscope–viewing biosignals on mobile devices. In: Joint International Conference on Pervasive Computing and the Networked World, pp 50–56

  10. Cabredo R, Legaspi RS, Inventado PS, Numao M (2012) An emotion model for music using brain waves. In: International Society of Music Information Retrieval, pp 265–270

  11. Campisi P, Scarano G, Babiloni F, Fallani FD, Colonnese S, Maiorana E, Forastiere L (2011) Brain waves based user recognition using the eyes closed resting conditions protocol. In: International Workshop on Information Forensics and Security, pp 1–6

  12. Champion C, Houghton S (2016) Application of continuous state hidden markov models to a classical problem in speech recognition. Comput Speech Lang 36:347–364

    Article  Google Scholar 

  13. Chuang J, Nguyen H, Wang C, Johnson B (2013) I think, therefore i am: Usability and security of authentication using brainwaves. In: International Conference on Financial Cryptography and Data Security, pp 1–16

  14. de Mira Jr. J, Neto HV, Neves EB, Schneider FK (2015) Biometric-oriented iris identification based on mathematical morphology. Journal of Signal Processing Systems 80(2):181–195

  15. Dustor A, Kłosowski P (2013) Biometric voice identification based on fuzzy kernel classifier. In: International Conference on Computer Networks, pp 456–465

  16. Hadjidimitriou SK, Hadjileontiadis LJ (2012) Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans Biomed Eng 59(12):3498–3510

    Article  Google Scholar 

  17. Hariadi M, Purnomo MH et al (2014) EEG signal identification based on root mean square and average power spectrum by using backpropagation. J Theor Appl Inf Technol 66(3)

  18. Holzinger A, Scherer R, Seeber M, Wagner J, Müller-Putz G (2012) Computational sensemaking on examples of knowledge discovery from neuroscience data: towards enhancing stroke rehabilitation. In: International Conference on Information Technology in Bio-and Medical Informatics, pp 166–168

  19. Iranmanesh V, Ahmad SMS, Adnan WAW, Yussof S, Arigbabu OA, Malallah FL (2014) Online handwritten signature verification using neural network classifier based on principal component analysis. Sci World J:2014

  20. Krishnan SR, Seelamantula CS (2013) On the selection of optimum savitzky-golay filters. IEEE Transactions on Signal Processing 61(2):380–391

    Article  MathSciNet  Google Scholar 

  21. Kumar A, Hanmandlu M, Madasu VK, Vasikarla S (2011) A palm print authentication system using quantized phase feature representation. In: Applied Imagery Pattern Recognition Workshop, pp 1–8

  22. La Rocca D, Campisi P, Scarano G (2013) On the repeatability of EEG features in a biometric recognition framework using a resting state protocol. In: BIOSIGNALS, pp 419–428

  23. Lalor EC, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB, Mcdarby G (2005) Steady-state VEP-based brain-computer interface control in an immersive 3d gaming environment. EURASIP journal on applied signal processing 2005:3156–3164

    MATH  Google Scholar 

  24. Lee JC (2012) A novel biometric system based on palm vein image. Pattern Recogn Lett 33(12):1520–1528

    Article  Google Scholar 

  25. Lin Y-P, Wang C-H, Wu T-L, Jeng S-K, Chen J-H (2008) Support vector machine for EEG signal classification during listening to emotional music. In: 10th Workshop on Multimedia Signal Processing, pp 127–130

  26. Mahajan K, Rai M (2014) Watermarking scheme for colour images using hidden markov model. International Journal of Computer Science and Mobile Computing

  27. Mandal R, Roy PP, Pal U, Blumenstein M (2015) Spatial pyramid matching-based multi-script off-line signature identification. Journal of the American Society of Questioned Document Examiners 18(1):3–9

    Google Scholar 

  28. Mao C, Hu B, Wang M, Moore P (2015) EEG-based biometric identification using local probability centers. In: International Joint Conference on Neural Networks, pp 1–8

  29. Nozaradan S, Peretz I, Missal M, Mouraux A (2011) Tagging the neuronal entrainment to beat and meter. J Neurosci 31(28):10234–10240

    Article  Google Scholar 

  30. Nozaradan S, Peretz I, Mouraux A (2012) Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J Neurosci 32(49):17572–17581

    Article  Google Scholar 

  31. Obermaier B, Guger C, Neuper C, Pfurtscheller G (2001) Hidden markov models for online classification of single trial EEG data. Pattern Recogn Lett 22(12):1299–1309

    Article  MATH  Google Scholar 

  32. Palaniappan R (2008) Two-stage biometric authentication method using thought activity brain waves. Int J Neural Syst 18(01):59–66

    Article  Google Scholar 

  33. Palaniappan R, Mandic DP (2007) Biometrics from brain electrical activity: A machine learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4):738–742

    Article  Google Scholar 

  34. Pleva M, Kiktova E, Viszlay P, Bours P (2016) Acoustical keystroke analysis for user identification and authentication. In: 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA), pp 386–389

  35. Pushpalatha KN, Prajwal SS, Gautam AK, Shiva Kumar KB (2014) Offline signature verification based on contourlet transform and textural features using hmm. In: Recent Advances and Innovations in Engineering, pp 1–6

  36. Rafiee J, Rafiee M, Prause N, Schoen M (2011) Wavelet basis functions in biomedical signal processing. Expert Syst Appl 38(5):6190–6201

    Article  Google Scholar 

  37. Ramirez R, Palencia-Lefler M, Giraldo S, Vamvakousis Z (2015) Musical neurofeedback for treating depression in elderly people. Front Neurosci 9

  38. Ravi H, Sivanath SK (2013) A novel method for touch-less finger print authentication. In: International Conference on Technologies for Homeland Security, pp 147–153

  39. Repovš G (2010) Dealing with noise in eeg recording and data analysis. In: Informatica Medica Slovenica, vol 15, pp 18–25

  40. Roy PP, Bhowmick S, Pal U, Ramel JY (2012) Signature based document retrieval using ght of background information. In: International Conference on Frontiers in Handwriting Recognition, pp 225–230

  41. Sebastien M, JosR D (2007) Person authentication using brainwaves EEG and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4):743–752

    Article  Google Scholar 

  42. Shah PG, Vastare KC, Huang X, Srikumar A, Sreenivasa SM, Kumar APRM, Kodada KR (2015) Development of a novel EEG wave controlled security system. In: Seventh International Conference on Intelligent Computing and Information Systems, pp 116–120

  43. Soleymani M, Pantic M, Pun T (2012) Multimodal emotion recognition in response to videos. IEEE Trans Affect Comput 3(2):211–223

    Article  Google Scholar 

  44. Stickel C, Fink J, Holzinger A (2007) Enhancing universal access–EEG based learnability assessment. In: International Conference on Universal Access in Human-Computer Interaction, pp 813–822

  45. Stytsenko K, Jablonskis E, Prahm C (2011) Evaluation of consumer eeg device emotiv epoc. In: MEi: CogSci Conference 2011, Ljubljana

  46. Sundararajan A, Pons A, Sarwat AI (2015) A generic framework for EEG-based biometric authentication. In: 12th International Conference on Information Technology-New Generations, pp 139–144

  47. Swaminathan R, Prasad S (2016) Brain computer interface used in health care technologies. In: Next Generation DNA Led Technologies, pp 49–58

  48. Tatum IV WO (2014) Handbook of EEG interpretation. Demos Medical Publishing

  49. Ting W, Guo-zheng Y, Bang-hua Y, Hong S (2008) EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 41 (6):618–625

    Article  Google Scholar 

  50. Wang Y, Gao X, Hong B, Jia C, Gao S (2008) Brain-computer interfaces based on visual evoked potentials. IEEE Engineering in medicine and biology magazine 27(5):64–71

    Article  Google Scholar 

  51. Weston J, Watkins C (1998) Multi-class support vector machines. Technical report

  52. Wu X, Zhang D, Wang K (2006) Palm line extraction and matching for personal authentication. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 36(5):978–987

    Article  Google Scholar 

  53. Yeom S-K, Suk H-I, Lee S-W (2013) Person authentication from neural activity of face-specific visual self-representation. Pattern Recogn 46(4):1159–1169

    Article  Google Scholar 

  54. Zquete A, Quintela B, da Silva Cunha JP (2010) Biometric authentication using brain responses to visual stimuli. In: BIOSIGNALS, pp 103–112

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Authors and Affiliations

  1. Department of Computer Science and Engineering, DCRUST, Sonipat, India

    Barjinder Kaur & Dinesh Singh

  2. Department of Computer Science and Engineering, IIT, Roorkee, India

    Partha Pratim Roy

Authors
  1. Barjinder Kaur
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  2. Dinesh Singh
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  3. Partha Pratim Roy
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Correspondence to Barjinder Kaur.

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Kaur, B., Singh, D. & Roy, P.P. A Novel framework of EEG-based user identification by analyzing music-listening behavior. Multimed Tools Appl 76, 25581–25602 (2017). https://doi.org/10.1007/s11042-016-4232-2

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