Open Access
Issue
MATEC Web Conf.
Volume 140, 2017
2017 International Conference on Emerging Electronic Solutions for IoT (ICEESI 2017)
Article Number 01027
Number of page(s) 4
DOI https://doi.org/10.1051/matecconf/201714001027
Published online 11 December 2017
  1. Krishnan R. V., Mythuswamy R., & Sankar V., “Spinal cord injury repair research: A new combination treatment strategy,” International Journal of Neuroscience, 108: 201–207, 2003. [CrossRef] [Google Scholar]
  2. Craig A., Hancock K., Dickson H., & Chang E., “Immunizing against depression and anxiety following spinal cord injury,” Archives of Physical Medicine and Rehabilitation, 79: 375–377, 1998. [CrossRef] [Google Scholar]
  3. Widerstrom-Noga E., Felipe-Cuervo E., & Yezierski R., “Relationships among clinical characteristics of chronic pain after spinal cord injury,” Archives of Physical & Mental Rehabilitation, 82: 1191-1197, 2001. [CrossRef] [Google Scholar]
  4. Simon Frantz., “Embryonic stem cell pioneer Geron exits field, cut losses,” Nature Biotechnology, 30: 12-13, 2012. [CrossRef] [Google Scholar]
  5. Michael G., & Reaz Vawda., “Cellular treatments for spinal cord injury: The time is right for clinical trials,” Neurotherapuetics, 8: 704-720, 2011. [CrossRef] [Google Scholar]
  6. Lakany H., & Conway B.A., “Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach,” Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML, ICGST, Cairo, Egypt, 05(5): 124-127, 2005. [Google Scholar]
  7. Vaughan, T.M., “Brain-computer interface technology: a review of the second international meeting,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11:94–109, 2003. [CrossRef] [Google Scholar]
  8. Craig. D.A., Nguyen. H.T., “Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control,” Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE: 2544 – 2547, 2007. [CrossRef] [Google Scholar]
  9. J.A. Pineda., B.Z Allison., and A. Vankov., “The Effects of Self-Movement, Observation and Imagination on μ Rhythms and Readiness Potentials (RP’s): Towards a Brain-Computer Interface (BCI),” IEEE Transactions on Rehabilitation Engineering, 8:219-222, 2000. [CrossRef] [Google Scholar]
  10. Stancák, A., Pfurtscheller, G., “Desynchronization and recovery of b rhythms during brisk and slow self-paced finger movements in man,” Neuroscience Letters, 196: 21-24, 1995. [CrossRef] [Google Scholar]
  11. Cassim, F., Szurhaj, W., Sediri, H., Devos, D., Bourriez, J.L., Poirot, I., Derambure, P., Defebvre, L., Guieu, J.D., “Brief and sustained movements: differences in event-related (de)synchronization (ERD/ERS) patterns,” Clinical Neurophysiology, 111: 2032-2039, 2000. [CrossRef] [Google Scholar]
  12. Haeger-Ross, C., Schieber, M.H., “Quantifying the independence of human finger movements: comparisons of digits, hands and movement frequencies,” Journal of Neuroscience, 20(22):8542-8550, 2000. [CrossRef] [Google Scholar]
  13. Li, Y., Gao, X., Liu, H., Gao, S., “Classification of single-trial electroencephalogram during finger movement,” IEEE Transactions on Biomedical Engineering, 51(6): 1019-1025, 2004. [CrossRef] [Google Scholar]
  14. Erbil, N., Ungan, P., “Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements,” Brain Research, 1169: 44-56, 2007. [CrossRef] [Google Scholar]
  15. Pfurtscheller, G., Neuper, C., Pichler-Zalaudek, K., Edlinger, G., Lopes da Silva, F., “Do brain oscillations of different frequencies indicate interaction between cortical areas in humans?,” Neuroscience Letters, 286:66-68, 2000. [CrossRef] [Google Scholar]
  16. Neuper, C., Pfurtscheller, G., “Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas,” Clinical Neurophysiology, 112:2084-2097, 2001. [CrossRef] [Google Scholar]
  17. Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., Hallett, M., “Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG,” Clinical Neurophysiology, 118:2637-2655, 2007. [CrossRef] [Google Scholar]
  18. Müller-Putz, G.R., Zimmermann, D., Graimann, B., Nestinger, K., Korisek, G., Pfurtscheller, G., “Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients,” Brain Research, 1137:84-91, 2007. [CrossRef] [Google Scholar]
  19. Morash, V., Bai, O., Furlani, S., Lin, P., Hallett, M., “Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries,” Clinical Neurophysiology, 119:2570-2578, 2008. [CrossRef] [Google Scholar]
  20. Niedermeyer E, F. H. Lopes da Silva., “Electroencephalography: Basic principles, clinical applications and related fields,”. 3rd edition, Lippincott, Williams & Wilkins, Philadelphia, 1993. [Google Scholar]
  21. Guyton. A.C. Textbook of Medical Physiology, Sixth Edition, W.B.Saunders Co., Philadelphia, Pa.6: 675-680, 1981. [Google Scholar]
  22. Sutter, E.E., Tran, D., “The brain response interface: communication through visually induced electrical brain responses,” Journal of Microcomputer Applications 15:31-45, 1992. [CrossRef] [Google Scholar]
  23. Middendorf, M., McMillan, G., Calhoun, G., Jones, K.S., “Braine computer interfaces based on the steady-state visual-evoked response,” IEEE Transaction on Rehabilitation Engineering 8 (2):211-214, 2000. [CrossRef] [PubMed] [Google Scholar]
  24. Rockstroh, B., Elbert, T., Canavan, A., Lutzenberger, W., Birbaumer, N., “Slow Cortical Potentials and Behavior,” second ed. Urban and Schwarzenberg, Baltimore, MD, 1989. [Google Scholar]
  25. Birbaumer, N., “Slow cortical potentials: their origin, meaning, and clinical use,” In: van Boxtel, G.J.M., Bo€cker, K.B.E. (Eds.), Brain and Behavior Past, Present, and Future, Tilburg University Press, Tilburg, pp. 25-39, 1997. [Google Scholar]
  26. Donchin, E., Spencer, K.M., Wijesinghe, R., “The mental prosthesis: assessing the speed of a P300-based brain computer interface,” IEEE Transaction on Rehabilitation Engineering 8:174-179, 2000. [CrossRef] [Google Scholar]
  27. Li, Y., Nam, C.S., Shadden, B., Johnson, S., “A P300-based Brain-Computer Interface (BCI): Effects of Interface Type and Screen Size,” International Journal of Human-Computer Interaction 27 (1):52-68, 2011. [CrossRef] [Google Scholar]
  28. Neuper, C., Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., “Motor imagery and EEG-based control of spelling devices and neuroprostheses,” Progress in Brain Research 159:393-409, 2006. [CrossRef] [Google Scholar]
  29. Neuper, C., Scherer, R., Wriessnegger, S., Pfurtscheller, G., “Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface,” Clinical Neurophysiology 120:239-247, 2009. [CrossRef] [Google Scholar]
  30. Kennedy, P.R., Bakey, R.A.E., Moore, M.M., Adams, K., “Direct control of a computer from the human central nervous system,” IEEE Transaction on Rehabilitation Engineering 8:198-202, 2000. [Google Scholar]
  31. Antonio Castro, Fernando Diaz and Geert J. M. van Boxtel, “How does a short history of spinal cord injury affect movement-related brain potentials?,” European Journal of Neuroscience, 25:2927-2934, 2007. [CrossRef] [Google Scholar]
  32. P Boord, Y Tran, J Middleton, A Craig, “Levels of brain wave activity (8-13 Hz) in persons with spinal cord injury,” Spinal Cord. Houndsmills, 42(2): 73, 2004. [CrossRef] [Google Scholar]
  33. P Boord, P J Siddall, Y Tran, D Herbert, et al, “Electroencephalographic slowing and reduced reactivity in neuropathic pain following spinal cord injury,” Spinal Cord. Houndsmills, 46(2): 118-124, 2008. [CrossRef] [Google Scholar]
  34. Yongwoong Jeon, Chang S. Nam, Young-Joo Kim, Min Cheol Whang, “Event-related (De) synchronization (ERD/ERS) during motor imagery tasks: Implications for brainecomputer interfaces,” International Journal of Industrial Ergonomics, 41:428-436, 2011. [CrossRef] [Google Scholar]
  35. Pfurtscheller, G., Aranibar, A., "Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement," Electroencephalography and Clinical Neurophysiology 46:138-146, 1979. [CrossRef] [Google Scholar]
  36. Jean-Michel Guérit, “Neuromonitoring in the operating room: why, when, and how to monitor?,” Electroencephalography and Clinical Neurophysiology, 106(1): 1-21, 1998. [Google Scholar]
  37. Heba Lakany and B.A. Conway, "Classification of Wrist Movements using EEG-based Wavelets Features," Proceedings of IEEE Engineering in Medicine and Biology, 27:5405-5407, 2005. [Google Scholar]
  38. I. Navarro, F. Sepulveda, B. Hubais, "A Comparison of Time, Frequency and ICA Based Features and Five Classifiers for Wrist Movement Classification in EEG Signals,"Proceedings of the 2005 IEEE Engineering in Medicine and Biology,2118-2121, 2005. [Google Scholar]
  39. Guger,W.H., Ramoser, H. & Pfurtscheller,G., “Real-time EEG analysis with subject-spesific spatial pattern for brain computer interface,” IEEE Transaction on Rehabilitation Engineering, 8(4), 2000. [Google Scholar]
  40. Antonio Castro, Fernando Diaz and Geert J. M. van Boxtel, “How does a short history of spinal cord injury affect movement-related brain potentials?,” European Journal of Neuroscience, 25: 2927-2934, 2007. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.