EEG ANALYSIS FOR DIGIT RECOGNITION BY TACTILE AND VIBROTACTILE STIMULATIONS

Anwesha Khasnobish, Shreyasi Datta, Dwaipayan Sardar, Amit Konar, Dewaki N. Tibarewala, Atulya K. Nagar

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References

  1. [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G.Pfurtscheller & T. M. Vaughan, Brain–computer interfaces forcommunication and control, Clinical Neurophysiology, 113(6),2002, 767-791.
  2. [2] A. B. Schwartz, X. T. Cui, D. J. Weber & D. W. Moran,Brain-controlled Interfaces: Movement Restoration with NeuralProsthetics, Neuron, 52(1), 2006, 205–220.
  3. [3] L. Kauhanen, T. Palomäki, P. Jylänki, F. Aloise, MarnixNuttin & J. del R. Millan, Haptic feedback compared with visualfeedback for BCI, Proc. 3rd International BCI Workshop andTraining Course, 2006, 66-67.
  4. [4] A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya & N.V.Thakor, A Brain Computer Interface with Vibrotactilebiofeedback for Haptic Information, Journal ofNeuroEngineering and Rehabilitation, 4(1), 2007, 1-12.
  5. [5] F. Cincotti et al, Vibrotactile feedback for brain-computerinterface operation, Computational Intelligence andNeuroscience, 2007, 2007, 1-12.
  6. [6] E. A. Curran & M. J. Stokes, Learning to control brainactivity: a review of the production and control of EEGcomponents for driving brain–computer interface (BCI)systems, Brain and Cognition, 51(3), 2003, 326-336.
  7. [7] G. Dornhege, Towards brain-computer interfacing (MITPress, 2007).
  8. [8] J. Martinovic, R. Lawson & M. Craddock, Time course ofinformation processing in visual and haptic objectclassification, Frontiers in human neuroscience, 6, 2012.
  9. [9]M. Grunwald, Human haptic perception (Boston BaselBerlin: Birkhaeuser Verlag AG, 2008).
  10. [10] A. Khasnobish, A. Konar, D. N. Tibarewala, S.Bhattacharyya, and R. Janarthanan, Object Shape Recognitionfrom EEG Signals during Tactile and Visual Exploration, Proc.Pattern Recognition and Machine Intelligence, Springer BerlinHeidelberg, 2013, 459-464.273
  11. [11] S. Datta, A. Saha & A. Konar, Perceptual Basis of TextureClassification from tactile stimulus by EEG Analysis, Proc.National Conference on Brain and Consciousness, Kolkata,2013, 38-45.
  12. [12] R. S. Johansson, U. Landstrom & R. Lundstrom, Responsesof mechanoreceptive afferent units in the glabrous skin of thehuman hand to sinusoidal skin displacements, Brain research,244(1), 1982, 17-25.
  13. [13] K. A. Kaczmarek, J. G. Webster, P. Bach-y-Rita & W. J.Tompkins, Electrotactile and vibrotactile displays for sensorysubstitution systems, IEEE Transactions on BiomedicalEngineering, 38(1), 1991, 1-16.
  14. [14] H. Nicolau, J. Guerreiro, T. Guerreiro & L. Carriço,UbiBraille: designing and evaluating a vibrotactile Braille-reading device, Proc. 15th International ACM SIGACCESSConference on Computers and Accessibility, 2013, 1-8.
  15. [15] L. Kohli, M. Niwa, H. Noma, K. Susami, Y. Yanagida, R.W. Lindeman, K. Hosaka & Y. Kume, Towards effectiveinformation display using vibrotactile apparent motion,Proc. 2006 14th Symposium on Haptic Interfaces for VirtualEnvironment and Teleoperator Systems, 2006, 445-451.
  16. [16] B. Weber, S. Schatzle, T. Hulin, C. Preusche & B. Deml,Evaluation of a vibrotactile feedback device for spatial guidance,Proc. World Haptics Conference (WHC), 2011, 349-354.
  17. [17] Y. Yanagida, M. Kakita, R. W. Lindeman, Y. Kume & N.Tetsutani, Vibrotactile letter reading using a low-resolutiontactor array, Proc. 12th International Symposium on HapticInterfaces for Virtual Environment and Teleoperator Systems,2004, 400-406.
  18. [18] A. Schlögl, The electroencephalogram and the adaptiveautoregressive model: theory and applications, Germany:Shaker, 2000.
  19. [19] H. Nai-Jen & R. Palaniappan, Classification of mental tasksusing fixed and adaptive autoregressive models of EEG signals,Proc. 2nd International Conference of the IEEE EMBS onNeural Engineering, 2005, 633-636.
  20. [20] C. Chatfield, The analysis of time series, an introduction,sixth edition (New York: Chapman & Hall/CRC, 2004).
  21. [21] E. D. Übeyli, Combined neural network model employingwavelet coefficients for EEG signals classification, DigitalSignal Processing, 19(2), 2009, 297-308.
  22. [22] A. Khasnobish, S. Bhattacharyya, A. Konar, D.N.Tibarewala, S.D. Burman & R. Janarthanan, Positive andNegative Emotion Classification from Stimulated EEG signals,In Biomedical Engineering, (R.R. Galigekere, A.GRamakrishnan, J.K., Udupa (Eds), New Delhi: NarosaPublishing House, 2012) 114-119.
  23. [23] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche & B.Arnaldi, A review of classification algorithms for EEG-basedbrain–computer interfaces, Journal of neural engineering, 4(2),2007.
  24. [24] Tom M. Mitchell, Machine learning (McGraw Hill, 1997).
  25. [25] M. Teplan, Fundamentals of EEG measurement,Measurement Science Review, 2(2), 2002, 1-11.
  26. [26] C. Preuschhof, H. R. Heekeren, B. Taskin, T. Schubert &A. Villringer, Neural correlates of vibrotactile working memoryin the human brain, The Journal of Neuroscience, 26(51), 2006,13231-13239.
  27. [27] J. S. Kim, O. L. Kim, W. S. Seo, B. H. Koo, Y. Joo & D. S.Bai, Memory Dysfunctions after Mild and Moderate TraumaticBrain Injury : Comparison between Patients with and withoutFrontal Lobe Injury, Journal of Korean Neurosurgical Society,46(5), 2009, 459-467.
  28. [28] M. Grunwald, T. Weiss, W. Krause, L. Beyer, R. Rost, I.Gutberlet & Hermann-Josef Gertz, Theta power in the EEG ofhumans during ongoing processing in a haptic object recognitiontask, Cognitive Brain Research, 11(1), 2001, 33-37.
  29. [29] F. Song, Z. Guo & Dayong Mei, Feature selection usingprincipal component analysis, Proc. 2010 IEEE InternationalConference on System Science, Engineering Design andManufacturing Informatization (ICSEM), vol.1, 2010, 27-30.
  30. [30] A. Halder, A. Jati, G. Singh, A. Konar, A. Chakraborty, &R. Janarthanan, Facial Action Point Based Emotion Recognitionby Principal Component Analysis, Proc. InternationalConference on Soft Computing for Problem Solving (SocProS2011), 2011, 721-733.
  31. [31] www.nasanmedical.com
  32. [32] T. Fawcett, An introduction to ROC analysis, Patternrecognition letters, 27(8), 2006, 861-874.
  33. [33] N. G. Das, Statistical Methods (TataMcGrawHill, 2008).
  34. [34] Q. Wang, Y. Shen and J. Q. Zhang, A nonlinear correlationmeasure for multivariable data set, Physica D: NonlinearPhenomena, 200(3), 2005, 287-295.
  35. [35] A. Khasnobish, S. Datta, M. Pal, A. Konar, D. N.Tibarewala & R. Janarthanan, Correlation Analysis of ObjectShape Recognition from EEG and Tactile Signals, Proc. IEEEInternational Conference in Advances in Electrical Engineering(ICAEE) 2014, Vellore, India, January 2014.
  36. [36] M. Niwa, Y. Yanagida, H. Noma, K. Hosaka & Y. Kume,Vibrotactile apparent movement by DC motors and voice-coiltactors, Proc. 14th International Conference on Artificial Realityand Telexistence (ICAT), 2004, 126-131.
  37. [37] J. van Erp, Vibrotactile spatial acuity on the torso: effects oflocation and timing parameters, Eurohaptics Conference, 2005and Symposium on Haptic Interfaces for Virtual Environmentand Teleoperator Systems, 200, World Haptics 2005.
  38. [38] M. Niwa, R. W. Lindeman, Y. Itoh & F. Kishino,Determining appropriate parameters to elicit linear and circularapparent motion using vibrotactile cues, Proc. World Haptics2009-Third Joint EuroHaptics conference and Symposium onHaptic Interfaces for Virtual Environment and TeleoperatorSystems, 2009, 75-78.
  39. [39] www.atmel.in
  40. [40] J. Demsar, Statistical comparisons of classifiers overmultiple data sets, The Journal of Machine Learning Research,7, 2006, 1-30.

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