A review of classification algorithms for EEG-based brain-computer interfaces

Journal of Neural Engineering

Volumn 4, Issue 2, 2007, Pages

  Lotte, F ^a   Congedo, M ^b   Lecuyer A ^a   Lamarche, F ^a   Arnaldi, B ^a  

^a INRIA   (France)

^b ORANGE LABS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; HUMAN COMPUTER INTERACTION; SPECTRUM ANALYSIS;

BRAIN COMPUTER INTERFACES; CLASSIFICATION ALGORITHMS;

ELECTROENCEPHALOGRAPHY;

ALGORITHM; BAYES THEOREM; BRAIN COMPUTER INTERFACE; ELECTROENCEPHALOGRAM; MEDICAL RESEARCH; NERVE CELL NETWORK; PRACTICE GUIDELINE; PRIORITY JOURNAL; REVIEW;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; COMMUNICATION AIDS FOR DISABLED; ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS; PATTERN RECOGNITION, AUTOMATED; USER-COMPUTER INTERFACE;

EID: 34247179140     PISSN: 17412560     EISSN: 17412552     Source Type: Journal    
DOI: 10.1088/1741-2560/4/2/R01     Document Type: Review

References (87)

1. Wolpaw J R, Birbaumer N, McFarland D J, Pfurtscheller G and Vaughan T M 2002 Brain-computer interfaces for communication and control Clin. Neurophysiol. 113 767-91
2. Vaughan T M et al 2003 Brain-computer interface technology: a review of the second international meeting IEEE Trans. Neural Syst. Rehabil. Eng. 11 94-109
3. Kübler A, Kotchoubey B, Kaiser J, Wolpaw J R and Birbaumer N 2001 Brain-computer communication: unlocking the locked in Psychol. Bull. 127 358-75
4. McFarland D J, Anderson C W, Muller K-R, Schlogl A and Krusienski D J 2006 BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation IEEE Trans. Neural Syst. Rehabil. Eng. 14 135-8
5. Duda R O, Hart P E and Stork D G 2001 Pattern Recognition 2nd edn (New York: Wiley-Interscience)
6. Vallabhaneni A, Wang T and He B 2005 Brain computer interface Neural Engineering ed B He (Dordrecht: Kluwer) pp 85-122
7. McFarland D J and Wolpaw J R 2005 Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance IEEE Trans. Neural Syst. Rehabil. Eng. 13 372-9
8. Penny W D, Roberts S J, Curran E A and Stokes M J 2000 EEG-based communication: a pattern recognition approach IEEE Trans. Rehabil. Eng. 8 214-5
9. Jain A K, Duin R P W and Mao J 2000 Statistical pattern recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 22 4-37
10. Kaper M, Meinicke P, Grossekathoefer U, Lingner T and Ritter H 2004 BCI competition 2003-data set iib: support vector machines for the p300 speller paradigm IEEE Trans. Biomed. Eng. 51 1073-6
11. Pfurtscheller G, Neuper C, Flotzinger D and Pregenzer M 1997 EEG-based discrimination between imagination of right and left hand movement Electroencephalogr. Clin. Neurophysiol. 103 642-51
12. Chiappa S and Bengio S 2004 HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems European Symposium on Artificial Neural Networks ESANN
13. Millán J R and Mourĩo J 2003 Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project IEEE Trans. Neural Syst. Rehabil. Eng. 11 159-61
14. Pfurtscheller G, Neuper C, Schlogl A and Lugger K 1998 Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters IEEE Trans. Rehabil. Eng. 6 316-25
15. Wang T, Deng J and He B 2004 Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns Clin. Neurophysiol. 115 2744-53
16. Qin L, Ding L and He B 2004 Motor imagery classification by means of source analysis for brain-computer interface applications J. Neural Eng. 1 135-141
17. Kamousi B, Liu Z and He B 2005 Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis IEEE Trans. Neural Syst. Rehabil. Eng. 13 166-71
18. Congedo M, Lotte F and Lécuyer A 2006 Classification of movement intention by spatially filtered electromagnetic inverse solutions Phys. Med. Biol. 51 1971-89
19. Rakotomamonjy A, Guigue V, Mallet G and Alvarado V 2005 Ensemble of SVMs for improving brain computer interface p300 speller performances Int. Conf. on Artificial Neural Networks
20. Haselsteiner E and Pfurtscheller G 2000 Using time-dependant neural networks for EEG classification IEEE Trans. Rehabil. Eng. 8 457-63
21. Penny W D and Roberts S J 1999 EEG-based communication via dynamic neural network models Proc. Int. Joint Conf. on Neural Networks
22. Lemm S, Schafer C and 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 1077-80
23. Obermeier B, Guger C, Neuper C and Pfurtscheller G 2001 Hidden Markov models for online classification of single trial EEG Pattern Recognit. Lett. 1299-309
24. Ng A and Jordan M 2002 On generative versus discriminative classifiers: a comparison of logistic regression and naive Bayes Proc. Advances in Neural Information Processing
25. Rubinstein Y D and Hastie T 1997 Discriminative versus informative learning Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining
26. Rabiner L R 1989 A tutorial on hidden Markov models and selected applications in speech recognition Proc. IEEE 77 257-86
27. Vapnik V N 1999 An overview of statistical learning theory IEEE Trans. Neural Netw. 10 988-99
28. Breiman L 1998 Arcing classifiers Ann. Stat. 26 801-49
29. Friedman J H K 1997 On bias, variance, 0/1-loss, and the curse-of-dimensionality Data Min. Knowl. Discov. 1 55-77
30. Raudys S J and Jain A K 1991 Small sample size effects in statistical pattern recognition: recommendations for practitioners IEEE Trans. Pattern Anal. Mach. Intell. 13 252-64
31. Jain A K and Chandrasekaran B 1982 Dimensionality and sample size considerations in pattern recognition practice Handbook of Statistics vol 2 ed P R Krishnaiah and L N Kanal (Amsterdam: North-Holland)
32. Fukunaga K 1990 Statistical Pattern Recognition 2nd edn (New York: Academic)
33. Pfurtscheller G 1999 EEG event-related desynchronization (ERD) and event-related synchronization (ERS) Electroencephalography: Basic Principles, Clinical Applications and Related Fields 4th edn ed E Niedermeyer and F H Lopes da Silva (Baltimore, MD: Williams and Wilkins) pp 958-67
34. Bostanov V 2004 BCI competition 2003-data sets 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 1057-61
35. Garrett D, Peterson D A, Anderson C W and Thaut M H 2003 Comparison of linear, nonlinear, and feature selection methods for EEG signal classification IEEE Trans. Neural Syst. Rehabil. Eng. 11 141-4
36. Scherer R, Muller G R, Neuper C, Graimann B and Pfurtscheller G 2004 An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate IEEE Trans. Biomed. Eng. 51 979-84
37. Garcia G N, Ebrahimi T and Vesin J-M 2003 Support vector EEG classification in the Fourier and time-frequency correlation domains Conference Proc. 1st Int. IEEE EMBS Conf. on Neural Engineering
38. Blankertz B, Curio G and Müller K R 2002 Classifying single trial EEG: towards brain computer interfacing Adv. Neural Inf. Process. Syst. (NIPS 01) 14 157-64
39. Müller K R, Krauledat M, Dornhege G, Curio G and Blankertz B 2004 Machine learning techniques for brain-computer interfaces Biomed. Technol. 49 11-22
40. Burges C J C 1998 A tutorial on support vector machines for pattern recognition Knowl. Discov. Data Min. 2 121-67
41. Bennett K P and Campbell C 2000 Support vector machines: hype or hallelujah? ACM SIGKDD Explor. Newslett. 2 1-13
42. Schlögl A, Lee F, Bischof H and Pfurtscheller G 2005 Characterization of four-class motor imagery EEG data for the BCI-competition 2005 J. Neural Eng. 2 L14-22
43. Hiraiwa A, Shimohara K and Tokunaga Y 1990 EEG topography recognition by neural networks IEEE Eng. Med. Biol. Mag. 9 39-42
44. Anderson C W and Sijercic Z 1996 Classification of EEG signals from four subjects during five mental tasks Solving Engineering Problems with Neural Networks: Proc. Int. Conf. on Engineering Applications of Neural Networks (EANN'96)
45. Bishop C M 1996 Neural Networks for Pattern Recognition (Oxford: Oxford University Press)
46. Palaniappan R 2005 Brain computer interface design using band powers extracted during mental tasks Proc. 2nd Int. IEEE EMBS Conf. on Neural Engineering
47. Balakrishnan D and Puthusserypady S 2005 Multilayer perceptrons for the classification of brain computer interface data Proc. IEEE 31st Annual Northeast Bioengineering Conference
48. Wang Y, Zhang Z, Li Y, Gao X, Gao S and 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 1081-6
49. Millán J R, Mourĩo J, Cincotti F, Babiloni F, Varsta M and Heikkonen J 2000 Local neural classifier for EEG-based recognition of mental tasks IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks
50. Millán J R, Renkens F, Mourino J and Gerstner W 2004 Noninvasive brain-actuated control of a mobile robot by human EEG IEEE Trans. Biomed. Eng. 51 1026-33
51. Solhjoo S and Moradi M H 2004 Mental task recognition: a comparison between some of classification methods BIOSIGNAL 2004 Int. EURASIP Conference
52. Cincotti F, Scipione A, Tiniperi A, Mattia D, Marciani M G, Millán J del R, Salinari S, Bianchi L and Babiloni F 2003 Comparison of different feature classifiers for brain computer interfaces Proc. 1st Int. IEEE EMBS Conf. on Neural Engineering
53. Kohonen T 1990 The self-organizing maps Proc. IEEE 78 1464-80
54. Pfurtscheller G, Flotzinger D and Kalcher J 1993 Brain-computer interface-a new communication device for handicapped persons J. Microcomput. Appl. 16 293-9
55. Carpenter G A, Grossberg S, Markuzon N, Reynolds J H and Rosen D B 1992 Fuzzy artmap: a neural network architecture for incrementalsupervised learning of analog multidimensional maps IEEE Trans. Neural Netw. 3 698-713
56. Palaniappan R, Paramesran R, Nishida S and Saiwaki N 2002 A new brain-computer interface design using fuzzy artmap. IEEE Trans. Neural Syst. Rehabil. Eng. 10 140-8
57. Barreto A B, Taberner A M and Vicente L M 1996 Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface Southeastcon '96. 'Bringing Together Education, Science and Technology', Proc. IEEE
58. Hoya T, Hori G, Bakardjian H, Nishimura T, Suzuki T, Miyawaki Y, Funase A and Cao J 2003 Classification of single trial EEG signals by a combined principal + independent component analysis and probabilistic neural network approach Proc. ICA2003 pp 197-202
59. Kostov A and Polak M 2000 Parallel man-machine training in development of EEG-based cursor control IEEE Trans. Rehabil. Eng. 8 203-5
60. Felzer T and Freisieben B 2003 Analyzing EEG signals using the probability estimating guarded neural classifier IEEE Trans. Neural Syst. Rehabil. Eng. 11 361-71
61. Tavakolian K and Rezaei S 2004 Classification of mental tasks using Gaussian mixture Bayesian network classifiers Proc. IEEE Int. Workshop on Biomedical Circuits and Systems
62. Rezaei S, Tavakolian K, Nasrabadi A M and Setarehdan S K 2006 Different classification techniques considering brain computer interface applications J. Neural. Eng. 3 139-44
63. Keirn Z A and Aunon J I 1990 A new mode of communication between man and his surroundings IEEE Trans. Biomed. Eng. 37 1209-14
64. Barreto G A, Frota R A and de Medeiros F N S 2004 On the classification of mental tasks: a performance comparison of neural and statistical approaches Proc. IEEE Workshop on Machine Learning for Signal Processing
65. Obermaier B, Neuper C, Guger C and Pfurtscheller G 2000 Information transfer rate in a five-classes brain-computer interface IEEE Trans. Neural Syst. Rehabil. Eng. 9 283-8
66. Solhjoo S, Nasrabadi A M and Golpayegani M R H 2005 Classification of chaotic signals using HMM classifiers: EEG-based mental task classification Proc. European Signal Processing Conference
67. Borisoff J F, Mason S G, Bashashati A and Birch G E 2004 Brain-computer interface design for asynchronous control applications: improvements to the lf-asd asynchronous brain switch IEEE Trans. Biomed. Eng. 51 985-92
68. Boostani R and Moradi M H 2004 A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier J. Neural Eng. 1 212-7
69. Hoffmann U, Garcia G, Vesin J-M, Diserens K and Ebrahimi T 2005 A boosting approach to p300 detection with application to brain-computer interfaces Conference Proc. 2nd Int. IEEE EMBS Conf. Neural Engineering
70. Qin J, Li Y and Cichocki A 2005 ICA and committee machine-based algorithm for cursor control in a BCI system Lecture Notes in Computer Science
71. Wolpert D H 1992 Stacked generalization Neural Netw. 5 241-59
72. Lee H and Choi S 2003 PCA+HMM+SVM for EEG pattern classification Proc. 7th Int. Symp. on Signal Processing and its Applications
73. Bezdec J C and Pal S K 1992 Fuzzy Models For Pattern Recognition (New York: IEEE)
74. Lotte F 2006 The use of fuzzy inference systems for classification in EEG-based brain-computer interfaces Proc. 3rd Int. Brain-Computer Interface Workshop and Training Course pp 12-13
75. Schlogl A, Neuper C and Pfurtscheller G 2002 Estimating the mutual information of an EEG-based brain-computer interface Biomed. Tech. 47 3-8
76. Mason S G and Birch G E 2000 A brain-controlled switch for asynchronous control applications IEEE Trans. Biomed. Eng. 47 1297-307
77. Xu W, Guan C, Siong C E, Ranganatha S, Thulasidas M and Wu J 2004 High accuracy classification of EEG signal 17th Int. Conf. on Pattern Recognition (ICPR'04) vol 2 pp 391-4
78. Thulasidas M, Guan C and Wu J 2006 Robust classification of EEG signal for brain-computer interface IEEE Trans. Neural Syst. Rehabil. Eng. 14 24-9
79. Tavakolian K, Vasefi F, Naziripour K and Rezaei S 2006 Mental task classification for brain computer interface applications Proc. Canadian Student Conf. on Biomedical Computing
80. Schalk G, McFarland D J, Hinterberger T, Birbaumer N and Wolpaw J R 2004 BCI2000: a general-purpose brain-computer interface (BCI) system IEEE Trans. Biomed. Eng. 51 1034-43
81. Arrouët C, Congedo M, Marvie J E, Lamarche F, Lecuyer A and Arnaldi B 2004 Open-vibe : a 3D platform for real-time neuroscience J. Neurother.
82. Blankertz B et al 2004 The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials IEEE Trans. Biomed. Eng. 51 1044-51
83. Anderson C W, Stolz E A and Shamsunder S 1998 Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks IEEE Trans. Biomed. Eng. 45 277-86
84. Garcia G, Ebrahimi T and Vesin J-M 2002 Classification of EEG signals in the ambiguity domain for brain computer interface applications 14th Int. Conf. on Digital Signal Processing (DSP2002)
85. Kalcher J, Flotzinger D, Neuper C, Golly S and Pfurtscheller G 1996 Graz brain-computer interface ii: towards communication between humans and computers based on online classification of three different EEG patterns Med. Biol. Eng. Comput. 34 383-8
86. Blanchard G and Blankertz B 2004 BCI competition 2003-data set iia: spatial patterns of self-controlled brain rhythm modulations IEEE Trans. Biomed. Eng. 51 1062-6
87. Mensh B D, Werfel J and 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 1052-6