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A Comprehensive Survey of Brain Interface Technology Designs

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Abstract

In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.

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Notes

  1. To date, the terms Brain–Computer Interface (BCI), Brain–Machine Interface (BMI), Direct Brain Interface (DBI) and Adaptive Brain Interface (ABI) have all been used to describe human interface technology controlled by signals measured directly from the brain. In terms of high-level design, there is essentially no difference between the technologies referred to by these terms. Even though DBI is the most generic term, we have chosen to avoid using any of these terms to reduce interpretation bias in this work. Instead we will us the term Brain Interface as a collective term for this approach to interface technology.

Abbreviations

AT:

acronym for assistive (or augmentative) technology

Attribute Sub-Class:

sub-category of a design attribute. See Design Attribute. For examples, see Table 2

Assistive Device:

the component of a Brain Interface (BI) that interacts directly with objects or people in the environment. For example, a speech synthesizer or an FES-based neuroprosthetic

BI:

acronym for Brain Interface

BI AT:

acronym for assistive (or augmentative) technology (AT) based on a Brain Interface (BI)

BI Transducer:

a component of a Brain Interface that translates a person’s brain activity into usable control signals as shown in Fig. 1b–f. Functionally similar to other transducers like joysticks and switches

Bio-recording Technology:

the class of equipment (sensors, amplifiers, converters and filters) used to measure a person’s brain activity in a BI Transducer

Continuous (fixed reference):

a signal classification; a signal of this class is a sequence of continuous amplitude values relative to a fixed reference value-like the adjustable level produced by an analog potentiometer. See other signal classes: Relative Continuous (no reference), Discrete (...) and Spatial Reference

Control Interface:

a component that is added to a BI Transducer that produces a relatively low dimensional output in order to expand the control dimensionality to a level required by an Assistive Device as depicted in Fig. 1c and f. See Table  1 for examples.

Demonstration System:

an experimental system (depicted in Fig.  1e and f) that demonstrates control of a BI Transducer but does not otherwise perform any useful function

Demo Device:

a device used to test the controllability of a BI technology in a demonstration system (see Fig. 1e and f). For example, a model vehicle or table-mounted robotic arm

Design Attribute:

an attribute of a Brain Interface technology design. See Table 1 for the list of design attributes

Discrete (with 1 NC state):

a signal classification; a signal of this class is a sequence of discrete states including one state that corresponds to the No Control state in the user. See No Control

Discrete (all IC states):

a signal classification; a signal of this class is a sequence of discrete states where all states corresponds to intentional control in the user. See Intentional Control

Discrete (with 1 unknown state):

a signal classification; a signal of this class is a sequence of discrete states where one state (the “unknown” state) is reserved for uncertain classifications

Endogenous Transducer:

a BI Transducer design that responds to spontaneous control signals from the user

Exogenous Transducer:

a BI Transducer design that responds to control signals evoked from the user using an external stimulator

Feature Extractor:

a component of a BI Transducer that translates the input brain signal into a feature vector correlated to a neurological phenomenon. This component is sometimes referred to as noise reduction, filtering, preprocessing or spike detection/sorting

Feature Translator:

a component of a BI Transducer that translates the feature vector into a useful control signal. This component is sometimes referred to as a Feature Classifier, Classifier or “decoding function” (or something similar)

Intentional Control:

a user state when the user is attempting to affect the output of the Brain Interface

IC:

acronym for Intentional Control

Neurological Phenomenon:

the phenomenon (or phenomena) used to control a BI Transducer. For example, a P300 response in EEG to an oddball stimulus is a well-studied phenomenon employed in several BI Transducers. Another well-known phenomenon is the increase in neural firing rates measured in microelectrodes as neural activity increases

No Control:

a user state when the user is not attempting to affect the output of the Brain Interface. For example, resting, monitoring, thinking, and daydreaming are all possible No Control states

NC:

acronym for No Control

Relative Continuous (no reference):

a signal classification; a signal of this class is a sequence of changes to previous amplitude values-like the output of a mouse. See Continuous (fixed reference)

Spatial Reference:

a signal classification; a signal of this class is a sequence of 2-D spatial positions (similar to signals output by an eye tracker, a touchscreen or stylus mechanism)

References

  1. Allison B. Z., Pineda J. A. (2003) ERPs evoked by different matrix sizes: Implications for a brain computer interface (BCI) system. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):110–113

    Google Scholar 

  2. Allison B. Z., Pineda J. A. (2005) Effects of SOA and flash pattern manipulations on ERP’s, performance, and preference: Implication for a BCI system. J Psychophysiol. 59(2):127–140

    Google Scholar 

  3. Anderson C. W., Devulapalli S. V., Stolz E. A. (1995) EEG Signal Classification with Different Signal Representations. IEEE Workshop on Neural Networks for Signal in Processing, Cambridge, MA, USA, pp. 475–483

    Google Scholar 

  4. Babiloni, F., L. Bianchi, F. Semeraro, J. Millan, R. del, J. Mourino, A. Cattini, S. Salinari, M. G. Marciani, and F. Cincotti. Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes. In Proc. Annual International Conference of IEEE EMBS, pp. 651–654, 2001

  5. Babiloni F., Cincotti F., Bianchi L., Pirri G., Millan J.del R., Mourino J., Salinari S., Marciani M. G. (2001) Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers. Med. Eng. Phys. 23(5):323–328

    PubMed  CAS  Google Scholar 

  6. Babiloni F., Cincotti F., Lazzarini L., Millan J., Mourino J., Varsta M., Heikkonen J., Bianchi L., Marciani M. G. (2000) Linear classification of low-resolution EEG patterns produced by imagined hand movements. IEEE Trans. Rehab. Eng. 8(2):186–188

    CAS  Google Scholar 

  7. Balbale U. H., J. E. Higgins, S. L. Bement, S. P. Levine. Multi-channel analysis of human event-related Cortical Potentials for the development of a Direct Brain Interface. In Proc. 1st Joint BMES/EMBS Conference, p. 447, 1999

  8. Barreto A. B., A.M. Taberner, L. M. Vicente. Neural network classification of spatio-temporal EEG readiness potentials. In Proc. 15th Southern Biomedical Engineering Conference, pp. 73–76, 1996

  9. Bashashati A., R. K. Ward and G. E. Birch. A new design of the Asynchronous Brain Computer Interface using the knowledge of the path of features. In Proc. 2nd IEEE Conference on Neural Engineering, Arlington, VA, USA, pp. 101–104, 2005

  10. Bashashati A., R. K. Ward, G. E. Birch, M. R. Hashemi, and M. A. Khalilzadeh. Fractal dimension-based EEG Biofeedback system. In Proc. Annual International Conference of IEEE EMBS, Cancun, Mexico, pp. 2220–2223, 2003

  11. Bayliss J. D. (2003) Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):113–116

    Google Scholar 

  12. Bayliss J. D. and D. H. Ballard. Single trial P300 recognition in a virtual environment. In Proc CIMA’99. Soft Computing in Biomedicine, Rochester, NY, 1999

  13. Bayliss J. D., Ballard D. H. (2000) A virtual reality testbed for brain–computer interface research. IEEE Trans. Rehab. Eng. 8(2):188–190

    CAS  Google Scholar 

  14. Bayliss J.D., Inverso M.S., Tentler M.S. (2004) Changing the P300 brain computer interface. Cyber Psychol. Behav. 7(6):694–704

    Google Scholar 

  15. Birbaumer N., Kubler A., Ghanayim N., Hinterberger T., Perelmouter J., Kaiser J., Iversen I., Kotchoubey B., Neumann N., Flor H. (2000) The thought translation device (TTD) for completely paralyzed patients. IEEE Trans. Rehab. Eng. 8(2):190–193

    CAS  Google Scholar 

  16. Birch G.E., Bozorgzadeh Z., Mason S.G. (2002) Initial on-line evaluations of the LF-ASD Brain–Computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials. IEEE Trans. Neural. Sys. Reh. Eng. 10(4):219–224

    Google Scholar 

  17. Birch G. E., Lawrence P. D., Hare R. D. (1993) Single trial in processing of event related potentials using outlier information. IEEE Trans. Biomed. Eng. 40(1):59–73

    PubMed  CAS  Google Scholar 

  18. Blanchard G., Blankertz B. (2004) BCI Competition 2003 – Data set IIa: spatial patterns of self-controlled brain rhythm modulations. IEEE Trans. Biomed. Eng. 51(6):1062–1066

    PubMed  Google Scholar 

  19. Blankertz B., G. Curio, K. R. Müller. Classifying single trial EEG: Towards brain computer interfacing. In Proc. advances in NIPS, pp. 157–164, 2002

  20. Blankertz B., Dornhege G., Schäfer C., Krepki R., Kolmorgen J., Müller K.R., Kunzmann V., Losch F., Curio G. (2003) Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):127–131

    Google Scholar 

  21. Boostani R., Moradi M. H. (2004) A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J. Neural. Eng. 4(1):212–217

    Google Scholar 

  22. Borisoff J. F., Mason S. G., Bashashati A., Birch G. E. (2004) Brain–Computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch. IEEE Trans. Biomed. Eng. 51(6):985–992

    PubMed  Google Scholar 

  23. Bostanov V. (2006) BCI Competition 2003 – Data Set Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans. Biomed. Eng. 51(6):1057–1061

    Google Scholar 

  24. Bozorgzadeh, Z., G. E. Birch, and S. G. Mason. The LF-ASD BCI: On-line identification of imagined finger movements in spontaneous EEG with able-bodied subjects. In Proc ICASSP 2000, Istanbul, Turkey, pp. 109–113, 2000

  25. Burke D., S. Kelly, P. de Chazal and R. Reilly. A simultaneous filtering and feature extraction strategy for direct brain interfacing. In Proc. Annual International Conference of IEEE EMBS, pp. 279–280, 2002

  26. Burke D., Kelly S., Chazal P. de, Reilly R., Finucane C. (2005) A parametric feature extraction and classification strategy for brain–computer interfacing. IEEE Trans. Neural. Sys. Reh. Eng. 13(1):12–17

    Google Scholar 

  27. Calhoun G. L., and G. R. McMillan. EEG-based control for human–computer interaction. In Proc 3rd Symposium on Human Interaction with Complex Systems (HICS ’96), Dayton, Ohio, USA, pp. 4–9, 1996

  28. Carmena J. M., Lebedev M. A., Crist R. E., O’Doherty J. E., Santucci D. M., Dimitrov D. F., Patil P. G., Henriquez C. S., Nicolelis M. A. L. (2003) Learning to control a brain–machine interface for reaching and grasping by primates. Plos Biol. 1(2):193–208

    CAS  Google Scholar 

  29. Carmena J. M., Lebedev M. A., Henriquez C. S., Nicolelis M. A. (2005) Stable ensemble performance with single-neuron variability during reaching movments in primates. J. Neurosci. 25(46):10712–10716

    PubMed  CAS  Google Scholar 

  30. Chapin J. K., Moxon K. A., Markowitz R. S., Nicolelis M. A. (1999) Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2(7):664–670

    PubMed  CAS  Google Scholar 

  31. Cheng M., and S. Gao. An EEG-based cursor control system. In Proc. IEEE BMES/EMBS Conference on Serving Humanity, Advancing Technology, Atlanta, USA, pp. 669, 1999

  32. Cheng M., X. Gao, S. Gao, and W. Boliang. Stimulation frequency extraction in SSVEP-based brain–computer interface. In Proc. 1st International Conference on Neural Interface and Control, pp. 64–67, 2005

  33. Cheng M., Gao X., Gao S., Xu D. (2002) Design and implementation of a brain–computer interface with high transfer rates. IEEE Trans. Biomed. Eng. 49(10):1181–1186

    PubMed  Google Scholar 

  34. Cheng M., W. Jia, X. Gao, S. Gao and F. Yang. Mu-rhythm-based cursor control. Clin. Neurophys. 115(4):745–751, 2004

    Google Scholar 

  35. Cincotti F., Bianchi L., Millan J., Mourino J., Salinari S., Marciani M.G., Babiloni F. (2001) Brain computer interface: The use of low resolution surface laplacian and linear classifiers for the recognition of imagined hand movements. Med. Eng. Phys. 23(5):655–658

    Google Scholar 

  36. Cincotti F., Mattia D., Babiloni C., Carducci F., Salinari S., Bianchi L., Marciani M. G., Babiloni F. (2003) The use of EEG modifications due to motor imagery for brain–computer interfaces. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):131–133

    Google Scholar 

  37. Cincotti, F., A. Scipione, A. Timperi, D. Mattia, M. G. Marciani, J. Millan, S. Salinari, L. Bianchi and F. Babiloni. Comparison of different feature classifiers for brain computer interfaces. In Proc 1st IEEE Conference on Neural Engineering, pp. 645–647, 2003

  38. Costa E. J., Cabral E. F. Jr. (2000) EEG-based discrimination between imagination of left and right hand movements using Adaptive Gaussian Representation. Med. Eng. Phys. 22(5):345–348

    PubMed  CAS  Google Scholar 

  39. Coyle D., Prasad G., McGinnity T. M. (2005) A time–frequency approach to feature extraction for a brian-computer interface with a comparative analysis of performance measures. EURASIP J App Sig in Proc 19:3141–3151

    Google Scholar 

  40. Coyle S., Ward T., Markham C., McDarby G. (2004) On the suitability of near-infrared (NIR) systems for next-generation brain–computer interfaces. Physiol. Measure. 25(4):815–822

    Google Scholar 

  41. Curran E., Sykacek P., Stokes M., Roberts S. J., Penny W., Johnsrude I., Owen A. M. (2004) Cognitive tasks for driving a brain–computer interfacing system: A pilot study. IEEE Trans. Neural. Sys. Reh. Eng. 12(1):48–54

    Google Scholar 

  42. Darmanjian S., S. P. Kim, M. C. Nechbya, S. Morrison, J. Principe, J. Wessberg, M. A. Nicolelis. Bimodal brain–machine interface for motor control of robotic prosthetic. In Proc. IEEE Int. Conf. on Intelligent Robots and Systems, Las Vegas, NV, USA, pp. 3612–3617, 2003

  43. Donchin E., Spencer K. M., Wijesinghe R. (2000) The mental prosthesis: Assessing the speed of a P300-based brain–computer interface. IEEE Trans. Rehab. Eng. 8(2):174–179

    CAS  Google Scholar 

  44. Dornhege, G., B. Blankertz and G. Curio. Speeding up classification of multi-channel brain–computer interfaces: Common spatial patterns for slow cortical potentials. In Proc 1st IEEE Conference on Neural Engineering, pp. 591–594, 2003

  45. Dornhege G., Blankertz B., Curio G., Muller K. R. (2004) Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE. Trans. Biomed. Eng. 51(6):993–1002

    PubMed  Google Scholar 

  46. Erfanian, A., A. Erfani. ICA-based classification scheme for EEG-based brain–computer interface: The role of mental practice and concentration skills. In Proc. Annual International Conference of IEEE EMBS, pp. 235–238, 2004

  47. Fabiani G. E., McFarland D., Wolpaw J. R., Pfurtscheller G. (2004) Conversion of EEG activity into cursor movement by a Brain–Computer Interface (BCI). IEEE Trans. Neural. Syst. Rehabil. Eng. 12(3):331–338

    PubMed  Google Scholar 

  48. Farwell L. A., Donchin E. (1988) Talking off the top of your head: towards a mental prosthesis utilizing event-related brain potentials. Elect. Clin. Neurophysiol. 80:510–523

    Google Scholar 

  49. Fatourechi, M., A. Bashashati, J. F. Borisoff, G. E. Birch, R. K. Ward. Improving the performance of the LF-ASD brain computer interface by means of genetic algorithm. In Proc. IEEE Symposium on Signal in Processing and Information Technology, Rome, Italy, 2004

  50. Flotzinger, D., M. Pregenzer, G. Pfurtscheller. Feature selection with distinction sensitive learning vector quantisation and genetic algorithms. In Proc. IEEE International Conference on Neural Networks, ICNN ’94, pp. 3448–3451, 1994

  51. Foley J. D., van Dam A., Feiner S. K., Hughes J. F. (1990) Computer Graphics Principles and Practice, 2nd ed. Addison-Wesley Publishing Inc., New York

    Google Scholar 

  52. Fukada, S., D. Tatsumi, H. Tsujimoto and S. Inokuchi. Studies of input speed of word inputting system using event-related potential. In Proc. Annual International Conference of IEEE EMBS, pp. 1458–1460, 1998

  53. Gao, X., X. Neng, H. Bo, G. Shangkai, Y. Fusheng. Optimal selection of independent components for event-related brain electrical potential enhancement. In IEEE International Workshop on Biomedical Circuits and Systems, pp. S3–5/INV-1–4, 2004

  54. Gao X., Xu D., Cheng M., Gao S. (2003) A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):137–140

    Google Scholar 

  55. Gao Y., Black M. J., Bienenstock E., Shoham S., Donoghue J. P. (2002) Probabilistic inference of hand motion from neural activity in motor cortex. Adv. NIPS 14:1–8

    Google Scholar 

  56. Gao,Y., M. J. Black, E. Bienenstock, W. Wu, and J. P. Donoghue. A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions. In Proc. 1st IEEE Conference on Neural Engineering, pp. 189–192, 2003

  57. Garcia, G., T. Ebrahimi. Time–frequency-space kernel for single EEG-trial classification. In Proc. 5th Nordic Signal in Processing Symposium (NORSIG 2002), 2002

  58. Garcia G., T. Ebrahimi and J. M. Vesin. Classification of EEG signals in the ambiguity domain for brain computer interface applications. In Proc. 14th International Conference on Digital Signal in Processing, pp. 301–305, 2002

  59. Garcia, G., T. Ebrahimi and J. M. Vesin. Correlative exploration of EEG signals for direct brain–computer communications. In Proc. ICASSP 2003, pp. 816–819, 2003

  60. Garcia, G., T. Ebrahimi and J. M. Vesin. Support vector EEG classification in the Fourier and time–frequency correlation domains. In Proc. 5th Nordic Signal in Processing Symposium (NORSIG 2002), pp. 591–594, 2003

  61. Garrett D., Peterson D. A., Anderson C. W., Thaut M. H. (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):141–144

    Google Scholar 

  62. Glassman E. L. (2005) A wavelet-like filter based on neuron action potentials for analysis of Human Scalp Electroencephalographs. IEEE Trans. Biomed. Eng. 52(11):1851–1862

    PubMed  Google Scholar 

  63. Graimann, B., J. E. Huggins, S. P. Levine, G. Pfurtscheller. Detection of ERP and ERD/ERS patterns in single ECoG channels. In Proc. 1st IEEE Conference on Neural Engineering, Capri Island, Italy, pp. 614–617, 2003

  64. Graimann B., Huggins J.E., Levine S.P., Pfurtscheller G. (2004) Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis. IEEE Trans. Biomed. Eng. 51(6):954–962

    PubMed  Google Scholar 

  65. Graimann B., Huggins J. E., Schlogl A., Levine S. P. Pfurtscheller G. (2003) Detection of movement-related desynchronization patterns in ongoing single-channel electrocorticogram. IEEE Trans. Neural. Sys. Reh. Eng. 11(3):276–281

    Google Scholar 

  66. Guan, C., Y. Chen, J. Lin, Y. Yuan, and M. Huang. N2 components as features for brain computer interface. In Proc. 1st International Conference on Neural Interface and Control, pp. 45–49, 2005

  67. Guan, C., M. Thulasidas, J. Wu. High performance P300 speller for brain–computer interface. In IEEE International Workshop on Biomedical Circuits and Systems, pp. S3–5/INV-13–16, 2004

  68. Guger C., G. Müller, C. Neuper, G. Krausz, I. Niedermayer, G. Pfurtscheller. Brain–computer communication system: The EEG-based control of a hand orthesis in a quadriplegic patient. In Proc. 7th Int. Conf. on Computers Helping People with Special Needs, ICCHP 2000, 2000

  69. Guger C., Ramoser H., Pfurtscheller G. (2000) Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a brain–computer interface (BCI). IEEE Trans. Rehab. Eng. 8(4):447–456

    CAS  Google Scholar 

  70. Gysels E., Celka P. (2004) Phase synchronization for the recognition of mental tasks in a brain–computer interface. IEEE Trans. Neural. Syst. Rehabil. Eng. 12(4):406–415

    PubMed  Google Scholar 

  71. Haselsteiner E., Pfurtscheller G. (2000) Using time-dependent neural networks for EEG classification. IEEE Trans. Rehab. Eng. 8(4):457–463

    CAS  Google Scholar 

  72. Hatsopoulos N., Joshi J., O’Leary J.G. (2004) Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J. Neurophysiol. 92(2):1165–1174

    PubMed  Google Scholar 

  73. Hill, J. N., T. N. Lal, K. Bierig, N. Birbaumer, B. Scholkopf. Attentional modulation of auditory event-related potentials in a brain–computer interface. In IEEE International Workshop on Biomedical Circuits and Systems, pp. 17–19 2004

  74. Hinterberger T., Kubler A., Kaiser J., Neumann N., Birbaumer N. (2003) A brain–computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Elect. Clin. Neurophysiol. 114(3):416–425

    Google Scholar 

  75. Hinterberger T., Neumann N., Pham M., Kubler A., Grether A., Hofmayer N., Wilhelm B., Flor H., Birbaumer N. (2004) A multimodal brain-based feedback and communication system. Exp. Brain Res. 154(4):521–526

    PubMed  CAS  Google Scholar 

  76. Hu, J, J. Si, B. Olson and J. P. He. Principle component feature detector for motor cortical control. In Proc. Annual International Conference of IEEE EMBS, pp. 4021–4024, 2004

  77. Huan N.J., Palaniappan R. (2004) Neural network classification of autoregressive features from electroencephalogram signals for brain–computer interface design. J. Neural. Eng. 1:142–150

    PubMed  Google Scholar 

  78. Huan, N. J. and R. Palaniappan. Classification of Mental Tasks using fixed and adaptive autoregressive models of EEG signals. In Proc. Annual International Conference of IEEE EMBS, pp. 507–510, 2004

  79. Huggins J. E., Levine S. P., Bement S. L., Kushwaha R. K., Schuh L. A., Passaro E. A., Rohde M. M., Ross D. A., Elisevich K. V., Smith B. J. (1999) Detection of event-related potentials for development of a direct brain interface. J. Clin. Neurophysiol. 16(5):448–455

    PubMed  CAS  Google Scholar 

  80. Huggins, J. E., S. P. Levine, J. A. Fessler, W. M. Sowers, G. Pfurtscheller, B. Graimann, A. Schloegl, D. N. Minecan, R. K. Kushwaha, S. L. Bement, O. Sagher, L. A. Schuh. Electrocorticogram as the basis for a direct brain interface: Opportunities for improved detection accuracy. In Proc. 1st IEEE Conference on Neural Engineering, pp. 587–590, 2003

  81. Hung C. I., Lee P. L., Wu Y. T., Chen L. F., Yeh T. C., Hsieh J. C. (2005) Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers. Ann. Biomed. Eng. 33(8):1053–1070

    PubMed  Google Scholar 

  82. Isaacs R. E., Weber D. J., Schwartz A. B. (2000) Work toward real-time control of a cortical neural prothesis. IEEE Trans. Rehab. Eng. 8(2):196–198

    CAS  Google Scholar 

  83. Ivanova, I., G. Pfurtscheller, C. Andrew. AI-based classification of single-trial EEG data. In Proc. Annual International Conference of IEEE EMBS, pp. 703–704, 1995

  84. Jansen B. H., Allam A., Kota P., Lachance K., Osho A., Sundaresan K. (2004) An exploratory study of factors affecting single trial P300 detection. IEEE Trans. Biomed. Eng. 51(6):975–978

    PubMed  Google Scholar 

  85. Jia, W., X. Zhao, H. Liu, X. Gao, S. Gao and F. Yang. Classification of single trial EEG during motor imagery based on ERD. In Proc. Annual International Conference of IEEE EMBS, pp. 5–8, 2004

  86. Kaiser J., Kubler A., Hinterberger T., Neumann N., Birbaumer N. (2002) A non-invasive communication device for the paralyzed. Minimal Invasive Neurosurg. 45(1):19–23

    Google Scholar 

  87. Kaiser J., Perelmouter J., Iversen I. H., Neumann N., Ghanayim N., Hinterberger T., Kubler A., Kotchoubey B., Birbaumer N. (2001) Self-initiation of EEG-based communication in paralyzed patients. Elect. Clin. Neurophysiol. 112(3):551–554

    CAS  Google Scholar 

  88. Kalcher, J., D. Flotzinger, G. Pfurtscheller. A new approach to a brain–computer-interface (BCI) based on Learning Vector Quantization (LVQ3). In Proc. Annual International Conference of IEEE EMBS, pp. 1658–1659, 1992

  89. Kalcher, J., D. Flotzinger, G. Pfurtscheller. Graz brain–computer interface: An EEG-based cursor control system. In Proc. Annual International Conference of IEEE EMBS, pp. 1264–1265, 1993

  90. Kaper M., Meinicke P., Grossekathoefer U., Lingner T., Ritter H. (2004) BCI competition 2003-Data set IIb: Support vector machines for the P300 speller paradigm. IEEE Trans. Biomed. Eng. 51(6):1073–1076

    PubMed  Google Scholar 

  91. Kawakami, T., M. Inoue, Y. Kobayashi, K. Nakashima. Application of event related potentials to communication aids. In Proc. Annual International Conference of IEEE EMBS, pp. 2229–2231, 1996

  92. Keirn Z. A., Aunon J. I. (1990) A new mode of communication between man and his surroundings. IEEE Trans. Biomed. Eng. 37(12):1209–1214

    PubMed  CAS  Google Scholar 

  93. Kelly S., D. Burke, P. de Chazal and R. Reilly. Parametric models and spectral analysis for classification in brain–computer interfaces. In Proc. 14th International Conference on Digital Signal in Processing, pp. 307–310, 2002

  94. Kelly S. P., Lalor E.C., Finucane C., McDarby G., Reilly R. B. (2005) Visual spatial attention control in an independant brain–computer interface. IEEE Trans. Biomed. Eng. 52(9):1588–1596

    PubMed  Google Scholar 

  95. Kelly, S. P., E. C. Lalor, C. Finucane and R. Reilly. A comparison of covert and overt attention as a control option in a steady-state visual evoked potential-based brain computer interface. In Proc. Annual International Conference of IEEE EMBS, pp. 2–4, 2004

  96. Kelly, S. P., E. C. Lalor, J. J. Foxe. Independent brain computer interface control using visual spatial attention-dependent modulations of Parieto-occipital alpha. In Proc. 2nd IEEE Conference on Neural Engineering, pp. 5–8, 2005

  97. Kemere C., Shenoy K. V., Meng T. H. (2004) Model-based neural decoding of reaching movements: A maximum likelihood approach. IEEE Trans. Biomed. Eng. 51(6):925–932

    PubMed  Google Scholar 

  98. Kennedy P. R., Bakay R. A., Moore M. M., Adams K., Goldwaithe J. (2000) Direct control of a computer from the human central nervous system. IEEE Trans. Rehab. Eng. 8(2):198–202

    CAS  Google Scholar 

  99. Kim H. K., Kim S. S., Kim S. J. (2005) Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study. J. Neurosci. Methods 150(2):202–211

    PubMed  Google Scholar 

  100. Kim S. P., Rao Y. N., Erdogmus D., Sanchez J. C., Nicolelis M. A., Principe J. (2005) Determining patterns in neural activity for reaching movements using nonnegative matrix factorization. EURASIP J. App. Sig. Proc. 19:3113–3121

    Google Scholar 

  101. Kohlmorgen, J. and B. Blankertz. A simple generative model for single-trial EEG classification. In Proc. International Conference on Artificial Neural Networks, pp. 1156–1161, 2003

  102. Kostov, A. and M. Polak. Prospects of computer access using voluntary modulated EEG signal. In Proc. 1st Annual ECPD Symposium on Brain & Consciousness, pp. 233–236, 1997

  103. Kostov A., Polak M. (2000) Parallel man–machine training in development of EEG-based cursor control. IEEE Trans. Rehab. Eng. 8(2):203–205

    CAS  Google Scholar 

  104. Krauledat, M., G. Dornhege, B. Blankertz, F. Losch, G. Curio and K. R. Mueller. Improving speed and accuracy of Brain–Computer interfaces using readiness potential features. In Proc. Annual International Conference of IEEE EMBS, pp. 4511–4515, 2004

  105. Krausz G., Scherer R., Korisek G., Pfurtscheller G. (2003) Critical decision-speed and information transfer in the “Graz Brain–Computer Interface”. Appl. Psychophysiol. Biofeedback 28(3):233–240

    PubMed  CAS  Google Scholar 

  106. Kubler A., Kotchoubey B., Hinterberger T., Ghanayim N., Perelmouter J., Schauer M., Fritsch C., Taub E., Birbaumer N. (1999) The thought translation device: a neurophysiological approach to communication in total motor paralysis. Exp. Brain Res. 124(2):223–232

    PubMed  CAS  Google Scholar 

  107. Kubler A., Kotchoubey B., Kaiser J., Wolpaw J. R., Birbaumer N. (2001) Brain–computer communication: Unlocking the locked in. Psych. Bull. 127(3):358–375

    CAS  Google Scholar 

  108. Kubler A., Mushahwar A., Hochberg L.R., Donoghue J. P. (2006) BCI Meeting 2005 – Workshop on clinical issues and applications. IEEE Trans. Neural. Sys. Reh. Eng. 14(2):131–134

    CAS  Google Scholar 

  109. Kubler A., Nijboer F., Mellinger J., vaugh B.A., Pawelzik H., Schalk G., McFarland D.J., Birbaumer N., Wolpaw J.R. (2005) Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurology, 64(10):1775–1777

    CAS  Google Scholar 

  110. LaCourse, J. R., E. Wilson. Brainiac: A brain computer link. In Proc. IEEE Instrumentation & Measurement Technology Conference, pp. 587–592, 1995

  111. Lal T. N., Schroder M., Hinterberger T., Weston J., Bogdan M., Birbaumer N., Scholkopf B. (2004) Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51(6):1003–1010

    PubMed  Google Scholar 

  112. Lalor E. C., Kelly S. P., Finucane C., Burke R., Reilly R. B., McDarby G. (2005) Steady-state VEP-based brain–computer interface control in an immersive 3D gaming environment. EURASIP J. App. Sig. Proc. 19:3156–3164

    Google Scholar 

  113. Lee P. L., Wu C. H., Hsieh J. C., Wu Y. T. (2005) Visual evoked potential actuated brain computer interface: A brain-actuated cursor system. Electr. Letts 41(15):832–834

    Google Scholar 

  114. Leeb, R. and G. Pfurtscheller. Walking through a virtual city by thought. In Proc. Annual International Conference of IEEE EMBS, pp. 4503–4506, 2004

  115. Lemm S., Blankertz B., Curio G., Muller K. R. (2005) Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans. Biomed. Eng. 52(9):1541–1548

    PubMed  Google Scholar 

  116. Lemm S., Schafer C., Curio G. (2004) BCI competition 2003 – Data set III: Probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements. IEEE Trans. Biomed. Eng. 51(6):1077–1080

    PubMed  Google Scholar 

  117. Leuthardt E. C., Schalk G., Wolpaw J. R., Ojemann J. G., Moran D. W. (2004) A brain–computer interface using electrocorticographic signal in humans. J. Neural. Eng. 1:63–71

    PubMed  Google Scholar 

  118. Levine S. P., Huggins J. E., Bement S. L., Kushwaha R. K., Schuh L. A., Passaro E. A., Rohde M. M., Ross D. A. (1999) Identification of electrocorticogram patterns as the basis for a direct brain interface. J Clin. Neurophysiol. 16(5):439–447

    PubMed  CAS  Google Scholar 

  119. Li, Y., A. Cichocki, C. Guan and J. Qin. Sparse factorization proprecessing-based offline analysis for a cursor control experiment. In IEEE International Workshop on Biomedical Circuits and Systems, pp. S3–5/INV-5–8, 2004

  120. Li Y., Gao X., Liu H., Gao S. (2004) Classification of single-trial electroencephalogram during finger movement. IEEE Trans. Biomed. Eng. 51(6):1019–1025

    PubMed  Google Scholar 

  121. Lisogurski D., Birch G. E. (1998) Identification of finger flexions from continuous EEG as a brain computer interface. Eng. Med. Biol. 20(4):2004–2007

    Google Scholar 

  122. Mahmoudi, B. and A. Erfanian. Single-channel EEG-based prosthetic hand grasp control for amputee subjects, In Proc. Annual International Conference of IEEE EMBS, pp. 2406–2407, 2002

  123. Mason, S. G., A. Bashashati, M. Fatourechi and G. E. Birch. Conceptual models for brain-interface technology design. http://www.braininterface.org/published links/BI Design Framework.htm, 2005

  124. Mason S. G., Birch G. E. (2000) A brain-controlled switch for asynchronous control applications. IEEE Trans. Biomed. Eng. 47(10):1297–1307

    PubMed  CAS  Google Scholar 

  125. Mason S.G., Birch G.E. (2003) A general framework for Brain–Computer interface design. IEEE Trans. Neural. Sys. Reh. Eng. 11(1):70–85

    Google Scholar 

  126. Mason, S.G. and G.E. Birch. Temporal control paradigms for direct brain interfaces – rethinking the definition of asynchronous and synchronous. In Proc. HCI International, Las Vegas, USA, 2005

  127. Mason S. G., Bohringer R., Borisoff J. F., Birch G. E. (2004) Real-time control of a video game with a direct brain–computer interface. J. Clin. Neurophys. 21(6):404–408

    Google Scholar 

  128. Mason, S. G., J. Kronegg, J. E. Huggins, M. Fatourechi and A. Schloegl. Evaluating the performance of self-paced Brain Computer Interface Technology. http://www.bci-info.org//Research_Info/documents/articles/self_paced_tech_report-2006–05–19.pdf, 2006

  129. Mason, S. G., M. M. Moore and G. E. Birch. Designing pointing devices using Brain–Computer Interface Technology. In Proc. 1st Intl. IEEE EMBS Conference on Neural Engineering, Capri, Italy, pp. 610–613, 2003

  130. McFarland D., Neat G. W., Read R., Wolpaw J. R. (1993) An EEG-based method for graded cursor control. Pyschobiology 21:77–81

    Google Scholar 

  131. McFarland D., Wolpaw J. R. (1999) EEG-based communication and control: Short-term role of feedback. IEEE Trans. Rehab. Eng. 6(1):7–11

    Google Scholar 

  132. McFarland D. J., Sarnacki W. A., Wolpaw J. R. (2003) Brain–computer interface (BCI) operation: Optimizing information transfer rates. Biol. Psychol. 63(3):237–251

    PubMed  Google Scholar 

  133. McMillan, G. R. and G. L. Calhoun. Direct brain interface utilizing self-regulation of steady-state visual evoked response (SSVER). In Proc RESNA ‘95 Annual Conference, pp. 693–695, 1995

  134. Mensh B. D., Werfel J., Seung H.S. (2004) BCI Competition 2003 – Data set Ia: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Trans. Biomed. Eng. 51(6):1052–1056

    PubMed  Google Scholar 

  135. Middendorf M., McMillan G., Calhoun G., Jones K.S. (2004) Brain–computer interfaces based on the steady-state visual-evoked response. IEEE Trans. Rehab. Eng. 8(2):211–214

    Google Scholar 

  136. Millan J., Mourino J., Franze M., Cincotti F., Varsta M., Heikkonen J., Babiloni F. (2002) A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Networks 13(3):678–686

    Google Scholar 

  137. Millan, J. del R., J. Mourino, F. Cincotti, M. Varsta, J. Heikkonen, F. Topani, M. G. Marciani, K. Kaski, F. Babiloni. Neural Networks for robust classification of mental tasks. In Proc. Annual International Conference of IEEE EMBS, pp. 1380–1382, 2000

  138. Millan J. del R., J. Mourino, M. G. Marciani, F. Babiloni, F. Topani, I. Canale, J. Heikkonen and K. Kaski. Adaptive brain interfaces for physically-disabled people. In Proc. Annual International Conference of IEEE EMBS, pp. 2008–2011, 1998

  139. Millán, J. del R., F. Renkens, J. Mouriño and W. Gerstner. Non-invasive brain-actuated control of a Mobile Robot. In Proc. 18th Joint International Conference on Artificial Intelligence, Acapulco, Mexico, 2003

  140. J. Mouriño, S. Chiappa, R. Jané and Millán, J. del R. Evolution of the mental states operating a brain–computer interface. In Proc. International Federation Conference for Medical and Biological Engineering, pp. 600–601, 2002

  141. Müller, K. R., G. Curio, B. Blankertz and G. Dornhege. Combining features for BCI. In Proc. Advances in NIPS (NIPS 02), 2002

  142. Muller-Putz G. R., Scherer R., Brauneis C., Pfurtscheller G. (2005) Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural. Eng. 2(4):123–130

    PubMed  Google Scholar 

  143. Muller-Putz G. R., Scherer R., Pfurtscheller G., Rupp R. (2005) EEG-based neuroprosthesis control: A step towards clinical practice. Neurosci. Letts 382(1–2):169–174

    Google Scholar 

  144. Musallam S., Cornell B. D., Greger B., Scherberger H., Andersen R. A. (2004) Cognitive control signals for neural prosthetics. Science 305(5681):258–262

    PubMed  CAS  Google Scholar 

  145. Neuper C., Scherer R., Reiner M., Pfurtscheller G. (2005) Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res. Cogn. Brain Res. 25(3):668–677

    PubMed  Google Scholar 

  146. Neuper C., Schlögl A., Pfurtscheller G. (1999) Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. J. Clin. Neurophys. 16(4):373–382

    CAS  Google Scholar 

  147. Nielsen, J., Usability Engineering. Orlando, Florida: AP Professional, 2003

  148. Obermaier B., Guger C., Neuper C., Pfurtscheller G. (2001) Hidden Markov models for online classification of single trial EEG data. Pattern Recog. Letts 22:1299–1309

    Google Scholar 

  149. Obermaier B., Muller G. R., Pfurtscheller G. (2003) “Virtual keyboard” controlled by spontaneous EEG activity. IEEE Trans. Neural Sys. Reh. Eng. 11(4):422–426

    CAS  Google Scholar 

  150. Obermaier B., Munteanu C., Rosa A., Pfurtscheller G. (2001) Asymmetric hemisphere modeling in an offline brain–computer interface. IEEE Trans. Systems, Man Cybernet. Part C 31(4):536–540

    Google Scholar 

  151. Obermaier B., Neuper C., Guger C., Pfurtscheller G. (2001) Information Transfer Rate in a Five-Classes Brain–Computer Interface. IEEE Trans. Neural. Sys. Reh. Eng. 9(3):283–288

    CAS  Google Scholar 

  152. Olson B., Si J., Hu J., He J.P. (2005) Closed-loop cortical control of direction using support vector machines. IEEE Trans. Neural. Syst. Rehabil. Eng. 13(1):72–80

    PubMed  Google Scholar 

  153. Otto K. J., Vetter R. J., Marzullo T. C., Kipke D. R. Brain–machine interfaces in rat motor cortex: Implications of adaptive decoding algorithms. In Proc 1st IEEE Conference on Neural Engineering, Capri Island, Italy, pp. 100–103, 2003

  154. Palaniappan, R. Brain computer interface design using band powers extracted during mental tasks. In Proc. 2nd IEEE Conference on Neural Engineering, pp. 5–8, 2005

  155. Palaniappan R., Paramesran R., Nishida S., Saiwaki N. (2002) A new brain–computer interface design using Fuzzy ARTMAP. IEEE Trans. Neural. Sys. Reh. Eng. 10(3):140–148

    Google Scholar 

  156. Patil P. G., Carmena J. A., Nicolelis M. A., Turner D. A. (2004) Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain–machine interface. Neurosurgery 55(1):27–35

    PubMed  Google Scholar 

  157. Penny W. D., Roberts S. J., Curran E. A., Stokes M. J. (2000) EEG-based communication: A pattern recognition approach. IEEE Trans. Rehab. Eng. 8(2):214–215

    CAS  Google Scholar 

  158. Perelmouter J., Birbaumer N. (2000) A binary spelling interface with random errors. IEEE Trans. Rehab. Eng. 8(2):227–232

    CAS  Google Scholar 

  159. Peters B. O., Pfurtscheller G., Flyvbjerg H. (2001) Automatic differentiation of multichannel EEG signals. IEEE Trans. Biomed. Eng. 48(1):111–116

    PubMed  CAS  Google Scholar 

  160. Peterson D. A., Knight J. N., Kirby M. J., Anderson C. W., Thaut M. H. (2005) Feature selection and blind source seperation in an EEG-based brain–computer interface. EURASIP J. App. Sig. Proc. 19:3128–3140

    Google Scholar 

  161. Pfurtscheller G., Muller-Putz G. R., Pfurtscheller J., Rupp R. (2005) EEG-based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP J. App. Sig. Proc. 19:3152–3155

    Google Scholar 

  162. Pfurtscheller G., Guger C. (1999) Brain–computer communication system: EEG-based control of hand orthesis in a tetraplegic patient. Acta Chir. Austriaca 31:23–25

    Google Scholar 

  163. Pfurtscheller G., Kalcher J., Neuper C., Flotzinger D., Pregenzer M. (1996) On-line EEG classification during externally-paced hand movements using a neural network-based classifier. Elect. Clin. Neurophysiol. 99(5):416–425

    CAS  Google Scholar 

  164. Pfurtscheller G., Muller G. R., Pfurtscheller J., Gerner H. J., Rupp R. (2003) ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Letts 351(1):33–36

    CAS  Google Scholar 

  165. Pfurtscheller G., Neuper C., Schlogl A., Lugger K. (1998) Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehab. Eng. 6(3):316–325

    CAS  Google Scholar 

  166. Pineda J. A., Allison B. Z., Vankov A. (2002) The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP’s): Toward a brain–computer interface (BCI). IEEE Trans. Rehab. Eng. 8(2):219–222

    Google Scholar 

  167. Polak, M. and A. Kostov. Feature extraction in development of brain–computer interface: A case study. In Proc. Annual International Conference of IEEE EMBS, pp. 2058–2061, 1998

  168. Polikoff, J. B., H.T. Bunnell and W. Borkowski. Toward a P300-based Computer Interface. In Proc RESNA ‘95, pp. 178–180, 1995

  169. Pregenzer, M., G. Pfurtscheller. Distinction sensitive learning vector quantization (DSLVQ)-application as a classifier based feature selection method for a brain computer interface. In Proc. 4th International Conference on Artificial Neural Networks, pp. 433–436, 1995

  170. Pregenzer M., Pfurtscheller G. (1999) Frequency component selection for an EEG-based brain to computer interface. IEEE Trans. Rehab. Eng. 7(4):413–419

    CAS  Google Scholar 

  171. Qin L., Ding L., He B. (2004) Motor imagery classification by means of source analysis for brain–computer interface applications. J. Neural. Eng. 1(3):135–141

    PubMed  Google Scholar 

  172. Qin L., He B. (2005) A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications. J. Neural. Eng. 2:65–72

    PubMed  Google Scholar 

  173. Ramoser H., Muller-Gerking J., Pfurtscheller G. (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehab. Eng. 8(4):441–446

    CAS  Google Scholar 

  174. Rao, Y. N., S. P. Kim, J. C. Sanchez, D. Erdogmus, J. Principe, J. M. Carmena, M. A. Lebedev, M. A. Nicolelis. Learning mappings in brain machine interfaces with echo state networks. In Proc. ICASSP 2004, pp. 233–236, 2005

  175. Roberts S.J., Penny W. (2000) Real-time brain–computer interfacing: A preliminary study using Bayesian learning. Med. Biol. Eng. Comput. 38(1):56–61

    PubMed  CAS  Google Scholar 

  176. Rohde M. M., Bement S. L., Huggins J. E., Levine S. P., Kushwaha R. K., Schuh L. A. (2002) Quality estimation of subdurally recorded, event-related potentials based on signal-to-noise ratio. IEEE Trans. Biomed. Eng. 49(1):31–40

    PubMed  Google Scholar 

  177. Ryu, C., Y. Song, D. S. Yoo, S. Choi, S. S. Moon and J. H. Sohn, EEG-based discrimination between yes and no. In Proc 1st Joint BMES/EMBS Conference, pp. 444, 1999

  178. Santos, T. O., R. Caetano, J. Moniz and A. C. Rosa. Classification of movement cognitive potentials from EEG. In Proc. first joint BMES/EMBS Conference, pp. 449, 1999

  179. Scherer R., Muller G. R., Neuper C., Graimann B., Pfurtscheller G. (2004) An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans. Biomed. Eng. 51(6):979–984

    PubMed  Google Scholar 

  180. Schloegl, A., C. Neuper, G. Pfurtscheller. Subject specific EEG patterns during motor imaginary. In Proc. Annual International Conference of IEEE EMBS, pp. 1530–1532, 1997

  181. Schlogl A., Lee F., Bischof H., Pfurtscheller G. (2005) Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J. Neural. Eng. 2(4):L14–L22

    PubMed  Google Scholar 

  182. Schlögl, A., G. Pfurtscheller, B. Schack. Single-trial EEG analysis using an adaptive autoregressive model. In 4th International Symposium on Central Nervous Monitoring, 1996

  183. Schroder M., M. Bogdan, W. Rosenstiel, T. Hinterberger, N. Birbaumer. Automated EEG Feature Selection for Brain Computer Interfaces, in Proc 1st IEEE Conference on Neural Engineering, pp. 626–629, 2003

  184. Schroder M., Lal T. N., Hinterberger T., Bodgan M., Hill J. N., Birbaumer N., Rosenstiel W., Scholkopf B. (2005) Robust EEG channel selection across subjects for brain–computer interface. EURASIP J. App. Sig. Proc. 19:3103–3112

    Google Scholar 

  185. Serby H., Yom-Tov E., Inbar G.F. (2005) An improved P300-based brain–computer interface. IEEE Trans. Neural. Syst. Rehabil. Eng. 13(1):89–98

    PubMed  Google Scholar 

  186. Serruya M., Hatsopoulos N., Fellows M., Paninski L., Donoghue J. (2003) Robustness of neuroprosthetic decoding algorithms. Biol. Cybernet. 88(3):219–228

    Google Scholar 

  187. Serruya M. D., Hatsopoulos N. G., Paninski L., Fellows M. R., Donoghue J. P. (2002) Instant neural control of a movement signal. Nature 416(6877):141–142

    PubMed  CAS  Google Scholar 

  188. Sheikh H., McFarland D. J., Sarnacki W. A., Wolpaw J. R. (2003) Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans. Neurosci. Letts 345(2):89–92

    CAS  Google Scholar 

  189. Shneiderman B. (1998) Designing the user interface-strategies for effective human–computer interaction, 3rd ed. Addison-Wesley Longman Inc., New York

    Google Scholar 

  190. Sutter E. E. (1992) The brain response interface: communication through visually-induced electrical brain responses. J. Micro. Comp. App. 15:31–45

    Google Scholar 

  191. Sykacek P., Roberts S., Stokes M., Curran E., Gibbs M., Pickup L. (2003) Probabilistic methods in BCI research. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):192–195

    CAS  Google Scholar 

  192. Sykacek P., Roberts S.J., Stokes M. (2004) Adaptive BCI based on variational Bayesian Kalman filtering: An empirical evaluation. IEEE Trans. Biomed. Eng. 51(5):719–727

    PubMed  Google Scholar 

  193. Taylor D. M., Tillery S. I., Schwartz A. B. (2002) Direct cortical control of 3D neuroprosthetic devices. Science 296(5574):1829–1832

    PubMed  CAS  Google Scholar 

  194. Taylor D. M., Tillery S. I. H., Schwartz A. B. (2003) Information conveyed through brain-control: Cursor versus robot. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):195–199

    Google Scholar 

  195. Thulasidas, M., C. Guan, S. Ranganatha, J. K. Wu, X. Zhu and W. Xu. Effect of ocular artifact removal in brain computer interface accuracy. In Proc Annual International Conference of IEEE EMBS, pp. 4385–4388, 2004

  196. Townsend G., Graimann B., Pfurtscheller G. (2004) Continuous EEG classification during motor imagery – Simulation of an asynchronous BCI. IEEE Trans. Neural. Sys. Reh. Eng. 12(2):258–265

    Google Scholar 

  197. Trejo L. J., Wheeler K. R., Jorgensen C. C., Rosipal R., Clanton S. T., Matthews B., Hibbs A. D., Matthews R., Krupka M. (2003) Multimodal neuroelectric interface development. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):199–204

    Google Scholar 

  198. Varsta, M., J. Heikkonen, J. del R. Millan and J. Mourino. Evaluating the performance of three feature sets for brain–computer interfaces with an early stopping MLP commitee. In Proceeding of 15th International Conference on Pattern Recognition (ICPR’00), pp. 907–910, 2000

  199. Vaughan T., Heetderks W. J., Trejo L. J., Rymer W. Z., Wienrich M., Moore M. M., Kubler A., Dobkin B. H., Birbaumer N., Donchin E., Wolpaw E. W., Wolpaw J. R. (2003) Brain–computer interface technology: a review of the second international meeting. IEEE Trans. Neural. Sys. Reh. Eng. 11(2):94–109

    Google Scholar 

  200. Vaughan T.M., Wolpaw J.R., Donchin E. (1996) EEG-based communication: Prospects and problems. IEEE Trans. Rehab. Eng. 4(4):425–430

    CAS  Google Scholar 

  201. Vidal, J. J. Real-time detection of brain events in EEG. In Proc IEEE, Special Issue on Biol. Signal. Process. Anal. 65(5):633–641, 1977

  202. Wang T., Deng J., He B. (2004) Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Intl. Fed. Clin. Neurophys. 115(12):2744–2753

    Google Scholar 

  203. Wang, Y., W. Ruiping, G. Xiaorong and G. Shangkai. Brain–computer interface based on the high-frequency steady-state visual evoked potential. In Proc. 1st International Conference on Neural Interface and Control, pp. 37–39, 2004

  204. Wang, Y., Z. Zhang, X. Gao and S. Gao. Lead selection for SSVEP-based brain–computer interface. In Proc. Annual International Conference of IEEE EMBS, pp. 4507–4509, 2004

  205. Wang Y., Zhang Z., Li Y., Gao X., Gao S., Yang F. (2004) BCI Competition 2003 – Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG. IEEE Trans. Biomed. Eng. 51(6):1081–1086

    PubMed  Google Scholar 

  206. Wessberg J., Stambaugh C. R., Kralik J. D., Beck P. D., Laubach M., Chapin J. K., Kim J., Biggs S. J., Srinivasan M. A., Nicolelis M. A. (2000) Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408(6810):361–365

    PubMed  CAS  Google Scholar 

  207. Wolpaw E. W., McFarland D. (2004) Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Neuroscience 101(51):17849–17854

    CAS  Google Scholar 

  208. Wolpaw J. R., Birbaumer N., Heetderks W. J., McFarland D. J., Peckham P. H., Schalk G., Donchin E., Quatrano L. A., Robinson C. J., Vaughan T. M. (2000) Brain–computer interface technology: A review of the first international meeting. IEEE Trans. Rehab. Eng. 8(2):164–173

    CAS  Google Scholar 

  209. Wolpaw J. R., Birbaumer N., McFarland D. J., Pfurtscheller G., Vaughan T. M. (2002) Brain–computer interfaces for communication and control. Elect. Clin. Neurophysiol. 113(6):767–791

    Google Scholar 

  210. Wolpaw J. R., Flotzinger D., Pfurtscheller G., McFarland D. J. (1997) Timing of EEG-based cursor control. J. Clin. Neurophysiol. 14(6):529–538

    PubMed  CAS  Google Scholar 

  211. Wolpaw J. R., Loeb G. E., Allison B. Z., Donchin E., Feix do Nascimento O., Heetderks W. J., Nijboer F., Shain W. G., Turner J. N. (2006) BCI Meeting 2005 – Workshop on signals and recording methods. IEEE Trans. Neural. Sys. Reh. Eng. 14(2):138–141

    Google Scholar 

  212. Wolpaw J. R., McFarland D., Neat G. W., Forneris C. A. (1991) An EEG-based brain–computer interface for cursor control. Elect. Clin. Neurophysiol. 70:710–523

    Google Scholar 

  213. Wolpaw J. R., McFarland D. J. (1994) Multichannel EEG-based brain–computer communication. Elect. Clin. Neurophysiol. 90(6):444–449

    CAS  Google Scholar 

  214. Wu, W., M. J. Black, Y. Gao, E. Bienenstock, A. Shaikhounix, J. P. Donoghue. Neural decoding of cursor motion using a Kalman Filter. In Proc NIPS 2002, 2002

  215. Wu W., Black M. J., Mumford D., Gao Y., Bienenstock E., Donoghue J.P. (2004) Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Trans. Biomed. Eng. 51(6):933–942

    PubMed  Google Scholar 

  216. Xu N., Gao X., Hong B., Miao X., Gao S., Yang F. (2004) BCI Competition 2003 – Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications. IEEE Trans. Biomed. Eng. 51(6):1067–1072

    PubMed  Google Scholar 

  217. Xu, W., C. Guan, C. E. Siong, S. Ranganatha, M. Thulasidas and J. Wu. High accuracy classification of EEG signals. In Proc. 17th international conference on pattern recognition (ICPR’04), pp. 391–394, 2004

  218. Yom-Tov E., Inbar G.F. (2002) Feature selection for the classification of movements from single movement-related potentials. IEEE Trans. Neural. Sys. Reh. Eng. 10(3):170–177

    Google Scholar 

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Acknowledgements

This work was conducted at the Neil Squire Society Brain Interface Laboratory in Vancouver, Canada, with support from the Canadian Institutes of Health Research grant MOP-62711, the Natural Sciences and Engineering Research Council of Canada, grant 90278-02. The authors would like to recognize Jaimie Borisoff and Adriane Randolph for their insightful feedback during the preparation of the manuscript. We would like to acknowledge that the second and third authors made equal contributions to this work.

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Authors and Affiliations

  1. Neil Squire Society, Brain Interface Laboratory, 220–2250 Boundary Road, Burnaby, Canada, V5M 3Z3

    S. G. Mason & G. E. Birch

  2. Department of Electrical and Computer Engineering, The University of British Columbia, 2356 Main Mall, Vancouver, Canada, V6T 1Z4

    A. Bashashati, M. Fatourechi & G. E. Birch

  3. Department of Computer Systems, University of Technology, Sydney, Australia

    K. F. Navarro

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  1. S. G. Mason
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  2. A. Bashashati
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  3. M. Fatourechi
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  4. K. F. Navarro
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  5. G. E. Birch
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Corresponding author

Correspondence to S. G. Mason.

Appendix A: Identified Research Groups

Appendix A: Identified Research Groups

Table 4 lists the research groups used in this study identified by the last name of one principal investigator (PIs) from each group. For multi-institution projects, multiple PIs were listed. PIs were selected based on our knowledge or, for groups that were unfamiliar (marked with an * in the table), the last author from the reviewed papers was used. Affiliated institutions or projects are listed as the PIs latest published affiliation. (Our intent here is to provide a quick reference identifier for each group and this list is not meant as an accurate representation of principal investigators for these institutions or projects. We recognize that over time, researchers may change institutions from the ones listed).

TABLE 4. Research groups used in this study. IDs marked with a * indicate research groups that were unknown to the authors and the principal investigator ID was assumed to be last author from reviewed paper(s).
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Mason, S.G., Bashashati, A., Fatourechi, M. et al. A Comprehensive Survey of Brain Interface Technology Designs. Ann Biomed Eng 35, 137–169 (2007). https://doi.org/10.1007/s10439-006-9170-0

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