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

View Full Paper


  1. [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller & T. M. Vaughan, Brain–computer interfaces for communication 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 Neural Prosthetics, Neuron, 52(1), 2006, 205–220.
  3. [3] L. Kauhanen, T. Palomäki, P. Jylänki, F. Aloise, Marnix Nuttin & J. del R. Millan, Haptic feedback compared with visual feedback for BCI, Proc. 3rd International BCI Workshop and Training Course, 2006, 66-67.
  4. [4] A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya & N.V. Thakor, A Brain Computer Interface with Vibrotactile biofeedback for Haptic Information, Journal of NeuroEngineering and Rehabilitation, 4(1), 2007, 1-12.
  5. [5] F. Cincotti et al, Vibrotactile feedback for brain-computer interface operation, Computational Intelligence and Neuroscience, 2007, 2007, 1-12.
  6. [6] E. A. Curran & M. J. Stokes, Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems, Brain and Cognition, 51(3), 2003, 326-336.
  7. [7] G. Dornhege, Towards brain-computer interfacing (MIT Press, 2007).
  8. [8] J. Martinovic, R. Lawson & M. Craddock, Time course of information processing in visual and haptic object classification, Frontiers in human neuroscience, 6, 2012.
  9. [9]M. Grunwald, Human haptic perception (Boston Basel Berlin: Birkhaeuser Verlag AG, 2008).
  10. [10] A. Khasnobish, A. Konar, D. N. Tibarewala, S. Bhattacharyya, and R. Janarthanan, Object Shape Recognition from EEG Signals during Tactile and Visual Exploration, Proc. Pattern Recognition and Machine Intelligence, Springer Berlin Heidelberg, 2013, 459-464. 273
  11. [11] S. Datta, A. Saha & A. Konar, Perceptual Basis of Texture Classification 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, Responses of mechanoreceptive afferent units in the glabrous skin of the human 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 sensory substitution systems, IEEE Transactions on Biomedical Engineering, 38(1), 1991, 1-16.
  14. [14] H. Nicolau, J. Guerreiro, T. Guerreiro & L. Carriço, UbiBraille: designing and evaluating a vibrotactile Braillereading device, Proc. 15th International ACM SIGACCESS Conference 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 effective information display using vibrotactile apparent motion, Proc. 2006 14th Symposium on Haptic Interfaces for Virtual Environment 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-resolution tactor array, Proc. 12th International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2004, 400-406.
  18. [18] A. Schlögl, The electroencephalogram and the adaptive autoregressive model: theory and applications, Germany: Shaker, 2000.
  19. [19] H. Nai-Jen & R. Palaniappan, Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals, Proc. 2nd International Conference of the IEEE EMBS on Neural 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 employing wavelet coefficients for EEG signals classification, Digital Signal Processing, 19(2), 2009, 297-308.
  22. [22] A. Khasnobish, S. Bhattacharyya, A. Konar, D.N. Tibarewala, S.D. Burman & R. Janarthanan, Positive and Negative Emotion Classification from Stimulated EEG signals, In Biomedical Engineering, (R.R. Galigekere, A.G Ramakrishnan, J.K., Udupa (Eds), New Delhi: Narosa Publishing House, 2012) 114-119.
  23. [23] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche & B. Arnaldi, A review of classification algorithms for EEG-based brain–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 memory in 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 Traumatic Brain Injury : Comparison between Patients with and without Frontal 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 of humans during ongoing processing in a haptic object recognition task, Cognitive Brain Research, 11(1), 2001, 33-37.
  29. [29] F. Song, Z. Guo & Dayong Mei, Feature selection using principal component analysis, Proc. 2010 IEEE International Conference on System Science, Engineering Design and Manufacturing 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 Recognition by Principal Component Analysis, Proc. International Conference on Soft Computing for Problem Solving (SocProS 2011), 2011, 721-733.
  31. [31]
  32. [32] T. Fawcett, An introduction to ROC analysis, Pattern recognition 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 correlation measure for multivariable data set, Physica D: Nonlinear Phenomena, 200(3), 2005, 287-295.
  35. [35] A. Khasnobish, S. Datta, M. Pal, A. Konar, D. N. Tibarewala & R. Janarthanan, Correlation Analysis of Object Shape Recognition from EEG and Tactile Signals, Proc. IEEE International 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-coil tactors, Proc. 14th International Conference on Artificial Reality and Telexistence (ICAT), 2004, 126-131.
  37. [37] J. van Erp, Vibrotactile spatial acuity on the torso: effects of location and timing parameters, Eurohaptics Conference, 2005 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 200, World Haptics 2005.
  38. [38] M. Niwa, R. W. Lindeman, Y. Itoh & F. Kishino, Determining appropriate parameters to elicit linear and circular apparent motion using vibrotactile cues, Proc. World Haptics 2009-Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 2009, 75-78.
  39. [39]
  40. [40] J. Demsar, Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research, 7, 2006, 1-30.

Important Links:

Go Back