Fast multi-command SSVEP brain machine interface without training

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5164 LNCS, Issue PART 2, 2008, Pages 300-307

  Vasquez, Pablo Martinez ^a   Bakardjian, Hovagim ^a   Vallverdu, Montserrat ^b   Cichocki, Andrezj ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; PHASE INTERFACES;

ARTIFICIAL NEURAL NETWORKS; BRAIN-MACHINE INTERFACING; HEALTHY SUBJECTS; INTERNATIONAL CONFERENCES; MULTI-STAGE; REAL TIME; STEADY-STATE VISUAL EVOKED POTENTIALS; TRAINING PHASE;

NEURAL NETWORKS;

EID: 51849111426     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87559-8_31     Document Type: Conference Paper

References (13)

1. Wolpaw, J., Birmbaumer, N., McFarland, D., Pfurtscheller, G., Vaughan, T.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767-791 (2002)
2. Niedermeyer, E., Lopes da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Ch. 54, Lippincott Williams and Wilkins, Philadelphia, Pa, USA (1982)
3. Trejo, L.J., Rosipal, R., Matthews, B.: Brain-Computer Interfaces for 1-D and 2-D Cursor Control: Designs Using Volitional Control of the EEG Spectrum or Steady-State Visual Evoked Potentials. IEEE Transactions of Neural Systems and Rehabilitation Engineering 14(2) (2006)
4. Martinez, P., Bakardjian, H., Cichocki, A.: Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm. Computational Intelligence and Neuroscience, Article ID 94561, 9 (2007), doi:10.1155/2007/94561
5. Martinez, P., Bakardjian, H., Cichocki, A.: Multicommand Real-Time Brain Machine Interface Fully Online Multicommand MultiBrain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm. Computational Intelligence and Neuroscience, Article ID 94561, 9 (2007), doi:10.1155/2007/94561
6. Middendorf, M., McMillan, G.R., Calhoun, G.L., Jones, K.S.: Brain-computer interfaces based on the steady-state visual-evoked response. Neural Systems and Rehabilitation Engineering, IEEE Transactions on 8(2), 211-214 (2000)
7. Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S.: A practical VEP-based brain-computer interface. Neural Systems and Rehabilitation Engineering 14(2), 234-240 (2006)
8. Wang, R., Gao, X., Gao, S.: Frequency Selection for SSVEP-based Binocular Rivalry. In: Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, March 16-19, 2005, pp. 600-603 (2005)
9. Bellanger, M.G.: Adaptive Digital Filters and Signal Analysis. Marcel Dekker, New York (1987)
10. Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, West Sussex (2003)
11. Kronegg, J., Voloshynovskiy, S., Pun, T.: Analysis of bit-rate definitions for brain-computer interfaces. In: 2005 Int. Conf. on Human-computer Interaction (HCI 2005), Las Vegas, Nevada, USA (2005)
12. Reinsch, C.: Smoothing by spline functions. Numerische Mathematic 11, 141-156 (1983)
13. Rice, J., Rosenbblatt, M.: Smoothing splines: regression, derivatives and deconvo-lution. Annals of Statistics 11, 141-156 (1983)