Control of a 2-DoF robotic arm using a P300-based brain-computer interface

[1]     M. Higashima, K. Urata, Y. Kawasaki, Y. Maeda, N. Sakai, C. Mizukoshi, T. Nagasawa, T. Kamiya, N. Yamaguchi, and Y. Koshino, “P300 and the thought disorder factor extracted by factor-analytic procedures in schizophrenia,” Biological Psychiatry, vol. 44, no. 2, pp. 115–120, 1998.

[2]     J. Polich and K. L. Herbst, “P300 as a clinical assay: rationale, evaluation, and findings,” International Journal of Psychophysiology, vol. 38, no. 1, pp. 3–19, 2000.

[3]     R. Polikar, M. Greer, L. Udpa, and F. Keinert, “Multiresolution wavelet analysis of erps for the detection of Alzheimer's disease,” vol. 3, pp. 1301–1304, 1997.

[4]     A. R. Miller, C. Baratta, C. Wynveen, and J. P. Rosenfeld, “False memory: P300 amplitude, topography, and latency,” Running Head: P300 and False Recognition.

[5]     J. P. Rosenfeld, “Event-related potentials in the detection of deception, malingering, and false memories,” in Handbook of Polygraph Testing, M. Kleiner, Ed. New York: Academic Press, 2002.

[6]     L. A. Farwell and E. Donchin, “The truth will out: Interrogative polygraphy (lie detection) with event-related brain potentials,” Psychophysiology, vol. 28, no. 5, pp. 531–547, 1991.

[7]     J. B. Polikoff, H. T. Bunnell, and W. J. Borkowski Jr, “Toward a p300-based computer interface,” in RESNA95 Annual Conference and RESNAPRESS and Arlington Va, 1995, pp. 178–180.

[8]     L. Bi, X. A. Fan, and Y. Liu, “Eeg-based brain-controlled mobile robots: A survey,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 2, pp. 161–176, March 2013.

[9]     M. A. Lebedev and M. A. Nicolelis, “Brain–machine interfaces: past, present and future,” TRENDS in Neurosciences, vol. 29, no. 9, pp. 536– 546, 2006.

[10] E. E. Thomson, R. Carra, and M. A. Nicolelis, “Perceiving invisible light through a somatosensory cortical prosthesis,” Nature Communications, vol. 4, no. 1482, 2013.

[11] P. J. Ifft, S. Shokur, Z. Li, M. A. Lebedev, and M. A. Nicolelis, “A brain-machine interface enables bimanual arm movements in monkeys,” Science translational medicine, vol. 5, no. 210, pp. 210 ra154–210ra154, 2013.

[12] J. Dethier, P. Nuyujukian, S. I. Ryu, K. V. Shenoy, and K. Boahen, “Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces,” Journal of neural engineering, vol. 10 , no. 3, p. 036008, 2013.

[13] N. Birbaumer and L. Cohen, “Brain-computer interfaces: Communication and restoration of movement in paralysis,” PhysiologyLondon, vol. 579, no. 3, pp. 621–636, 2007.

[14] N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. Kubler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralyzed,” Nature, vol. 398, pp. R1–R13, 1999.

[15] L. J. P. M. H. L. Stopczynski A, Stahlhut C, “The smartphone brain scanner: A portable real-time neuroimaging system,” PLoS ONE, vol. 9, no. 2, 2014.

[16] S.-S. Yoo, H. Kim, E. Filandrianos, S. J. Taghados, and S. Park, “Non-invasive brain-to-brain interface (bbi): establishing functional links between two brains,” PloS one, vol. 8, no. 4, p. e60410, 2013.

[17] J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, “An EEG-based brain-computer interface for cursor control,” Electroencephalogr.Clin. Neurophysiol., vol. 78, no. 3, pp. 252–259, 1991.

[18] Y. Li, C.Wang, H. Zhang, and C. Guan, “An EEG-based BCI system for 2d cursor control,” in Proc. IEEE Int. Joint Conf. Neural Netw., 2008.

[19] R. Krepki, B. Blankertz, G. Curio, and K. Muller, “The Berlin brain-computer interface (bbci): Towards a new communication channel for online control in gaming applications,” Computational and Mathematical Methods in Medicine, vol. 33, no. 1, pp. 73–90, 2007.

[20] M. A. Nicolelis and M. A. Lebedev, “Principles of neural ensemble physiology underlying the operation of brain-machine interfaces,” Nature Reviews Neuroscience, vol. 10, no. 7, pp. 530–540, 2009.

[21] A. J. Suminski, D. C., Tkach, A. H. Fagg, and N. G. Hatsopoulos, “Incorporating feedback from multiple sensory modalities enhances brain-machine interface control,” Journal of Neuroscience, vol. 30, no. 50, pp. 16777–16787, 2010.

[22] D. Sussillo and L. F. Abbott, “Generating coherent patterns of activity from chaotic neural networks,” Neuron, vol. 63, no. 4, pp. 544–557, 2009.

[23] A. H. Fagg, N. G. Hatsopoulos, V. de Lafuente, K. A. Moxon, S. Nemati, J. M. Rebesco, R. Romo, S. A. Solla, J. Reimer, D. Tkach et al., “Biomimetic brain-machine interfaces for the control of movement,” Journal of Neuroscience, vol. 27, no. 44, pp. 11842–11846, 2007.

[24] M. Thulasidas, C. Guan, and J. Wu, “Robust classification of EEG signal for brain-computer interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 1, pp. 24–29, March 2006.

[25] E. M. Ventouras, P. Asvestas, I. Karanasiou, and G. K. Matsopoulos, “Classification of error-related negativity (ern) and positivity (pe) potentials using knn and support vector machines,” Computers in Biology and Medicine, vol. 41, no. 2, pp. 98 – 109, 2011.

[26] A. Mahmoudi, S.Takerkart, F. Regragui, D. Boussaoud, and A. Brovelli, “Multivoxel pattern analysis for fMRI data: A review,” Computational and Mathematical Methods in Medicine, 2012.

[27] E. Donchin, K. M. Spencer, and R. Wijesinghe, “The mental prosthesis: assessing the speed of a p300-based brain-computer interface,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174–179, Jun 2000.

[28] Y. Li, H. Li, and C. Guan, “A self-training semi-supervised SVM algorithm and its application in an EEG-based brain-computer interface speller system,” Pattern Recognit. Lett, vol. 29, no. 9, pp. 1285–1294, 2008.

[29] L. Wang, G. Xu, S. Yang, W. Yan, et al., “Application of Hilbert-Huang transform for the study of motor imagery tasks,” pp. 3848–3851, 2008.

[30] W. Pan, S. Ji-Zhong, and S. Jin-He, “Research of p300 feature extraction algorithm based on wavelet transform and fisher distance,” International Journal of Education and Management Engineering (IJEME), vol. 1, no. 6, p. 36, 2011.

[31] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.

[32] N. E. Huang, Z. Shen, and S. R. Long, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London, A(454):903–995, 1998

[33] B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, “Robotics modeling, planning and control,” in Advanced Textbooks in Control and Signal Processing, 2008.

[34] H. K. Khalil, “Nonlinear systems,” Prentice Hall, 2002.

[35] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.

[36] B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, “Robotics modeling, planning and control,” in Advanced Textbooks in Control and Signal Processing, 2008.

[37]  Yalda Shahriari and Abbas Erfanian, “ImprovingtheperformanceofP300-based brain–computer interface through subspace-based filtering”, Neurocomputing 121, pp: 434-441, 2013.