Efficient automatic selection and combination of eeg features in least squares classifiers for motor imagery brain-computer interfaces

International Journal of Neural Systems

Volumn 23, Issue 4, 2013, Pages

  Rodríguez Bermúdez, German ^a   García Laencina, Pedro J ^a   Roca Dorda, Joaquín ^a  

^a Centro Universitario de la Defensa de San Javier   (Spain)

Author keywords

brain computer interface; Electroencephalographic signals; feature extraction and selection; least squares classification; motor imagery

Indexed keywords

COMPUTATIONAL REQUIREMENTS; ELECTROENCEPHALOGRAPHIC SIGNALS; FEATURE EXTRACTION AND SELECTION; LEAST ANGLE REGRESSIONS; LEAST SQUARE; LOW COMPUTATIONAL COMPLEXITY; MOTOR IMAGERY; STATE-OF-THE-ART APPROACH;

ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; INTERFACES (COMPUTER);

BRAIN COMPUTER INTERFACE;

ALGORITHM; ARTICLE; AUTOMATION; BRAIN; BRAIN COMPUTER INTERFACE; COMPUTER INTERFACE; ELECTROENCEPHALOGRAPHY; HUMAN; IMAGE PROCESSING; IMAGINATION; PHYSIOLOGY; REGRESSION ANALYSIS; TIME;

ALGORITHMS; AUTOMATION; BRAIN; BRAIN-COMPUTER INTERFACES; ELECTROENCEPHALOGRAPHY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGINATION; LEAST-SQUARES ANALYSIS; REGRESSION ANALYSIS; TIME FACTORS; USER-COMPUTER INTERFACE;

EID: 84879104009     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065713500159     Document Type: Article

References (88)

1. M. Ahmadlou, H. Adeli and A. Adeli, New diagnostic EEG markers of the alzheimer disease using visibility graph, J. Neural Transm. 117 (2010) 1099-1109.
2. M. Ahmadlou and H. Adeli, Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology, NeuroImage 58(2) (2011) 401-408.
3. M. Ahmadlou, H. Adeli and A. Adeli, Fractality analysis of frontal brain in major depressive disorder, Int. J. Psychophysiol. 85(2) (2012) 206-211.
4. U. R. Acharya, S. V. Sree, P. C. Alvin Ang, R. Yanti and J. S. Suri, Application of nonlinear and wavelet based features for the automated identification of epileptic EEG signals, Int. J. Neural Syst. 22(2) (2012) 1250002.
5. M. Ahmadlou, H. Adeli and A. Adeli, Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder, J. Neurosci. Methods 211(2) (2012) 203-209.
6. H. Adeli and S. Ghosh-Dastidar, Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology (CRC Press, Taylor & Francis Group, Boca Raton, Florida, 2010).
7. M. Ahmadlou, H. Adeli and A. Adeli, Spatiotemporal analysis of relative convergence (STARC) of EEGs reveals differences between brain dynamics of depressive women and men, Clin. EEG Neurosci. (2013), doi: 10.1177/ 1550059413480504.
8. M. Ahmadlou and H. Adeli, Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder, Clin. EEG Neurosci. 42 (2011) 6-13.
9. M. Ahmadlou, H. Adeli and A. Amir, Graph theoretical analysis of organization of functional brain networks in ADHD, Clin. EEG Neurosci. 53(1) (2012) 5-13.
10. M. Ahmadlou, H. Adeli and A. Adeli, Improved visibility graph fractality with application for the diagnosis of Autism spectrum disorder, Physica A: Stat. Mech. Appl. 391(20) (2012) 4720-4726.
11. M. Ahmadlou and H. Adeli, Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems, Physica D: Nonlinear Phenomena 241(4) (2012) 326-332.
12. Y. Tang and D. M. Durand, A novel electrical stimulation paradigm for the suppression of epileptiform activity in an in-vivo model of mesial temporal lobe status epilepticus, Int. J. Neural Syst. 22(3) (2011) 1250006-1250014.
13. P. Rajdev, M. Ward and P. Irazoqui, Effect of electrical stimulation parameters on seizure treatment in the kainate animal model, Int. J. Neural Syst. 21(2) (2011) 151-162.
14. T. S. Nelson, C. L. Suhr, D. R. Freestone, A. Lai, A. J. Halliday, K. J. McLean, A. N. Burkitt and M. J. Cook, Closed-loop seizure control with very high frequency electrical stimulation at seizure onset in the gears model of absence epilepsy, Int. J. Neural Syst. 21(2) (2011) 163-173.
15. A. F. Jahangiri and D. M. Durand, Phase resetting of spiking epileptiform activity by electrical stimulation in the CA3 region of the rat hippocampus, Int. J. Neural Syst. 21(2) (2011) 127-138.
16. W. G. Besio, X. Liu, L. Wang, A. V. Medvedev and K. Koka, Transcutaneous focal electrical stimulation via concentric ring electrodes reduces synchrony induced by pentylenetetrazole in beta and gamma bands in rats, Int. J. Neural Syst. 21(2) (2011) 139-149.
17. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy, IEEE Trans. Biomed. Eng. 54(2) (2007) 205-211. (Pubitemid 46188163)
18. A. V. Medvedev, A. M. Murro and K. J. Meador, Abnormal interictal gamma activity may manifest a seizure onset zone in temporal lobe epilepsy, Int. J. Neural Syst. 21(2) (2011) 103-114.
19. S. Colici, O. C. Zalay and B. L. Bardakjian, Response neuromodulators based on artificial neural networks used to control seizure-like events in a computational model of epilepsy, Int. J. Neural Syst. 21(5) (2011) 367-383.
20. D. Sherman, N. Zhang, M. Anderson, S. Garg, M. J. Hinich, N. V. Thakor and M. A. Mirski, Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy, Int. J. Neural Syst. 21(2) (2011) 115-126.
21. S. Ghosh-Dastidar and H. Adeli, Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Comput. Aided Eng. 14(3) (2007) 187-212. (Pubitemid 47191475)
22. S. Ghosh-Dastidar and H. Adeli, A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Netw. 22(10) (2009) 1419-1431.
23. H. Adeli, Z. Zhou and N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform, J. Neurosci. Methods 123(1) (2003) 69-87. (Pubitemid 36173654)
24. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer's disease: Models of computation and analysis of EEGs, Clin. EEG Neurosci. 36(3) (2005) 131-140. (Pubitemid 41153680)
25. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Alzheimer's disease and models of computation: Imaging, classification and neural models, J. Alzheimers Dis. 7(3) (2005) 187-199. (Pubitemid 40917248)
26. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of alzheimer's disease, Neu-rosci. Lett. 444 (2) (2008) 190-194.
27. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chaos methodology for EEG-based diagnosis of alzheimer's disease, Alzheimer Dis. Assoc. Disord. 25(1) (2011) 85-92.
28. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Voxel-based morphometry in alzheimer's patients, J. Alzheimer's Dis. 10(4) (2006) 445-447.
29. H. Adeli, Z. Sankari and A. Adeli, Intrahemi-spheric, interhemispheric, and distal EEG coherence in Alzheimer's disease, Clin. Neurophysiol. 122(5) (2011) 897-906.
30. Z. Sankari and H. Adeli, Probabilistic neural networks for diagnosis of alzheimer's disease using conventional and wavelet coherence, J. Neurosci. Methods 197(1) (2011) 165-170.
31. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller and T. M. Vaughan, Brain-computer interfaces for communication and control, Clin. Neurophysiol. 113(6) (2002) 767-791. (Pubitemid 34621187)
32. M. Schroder, M. Bogdan, T. Hinterberger and N. Birbaumer, Automated EEG feature selection for brain computer interfaces, in First Int. IEEE EMBS Conf. Neural Engineering, Capri, Italy, March (2003), pp. 626-629.
33. A. Bashashati, M. Fatourechi, R. K. Ward and G. E. Birch, A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, J. Neural Eng. 4(2) (2007) R32-R57. (Pubitemid 46603812)
34. T. J. Sejnowski, G. Dornhege, J. R. Millán, T. Hinterberger, D. J. McFarland and K.-R. Müller, Toward Brain-Computer Interfacing (Neural Information Processing) (The MIT Press, 2007).
35. A. Schlögl, C. Vidaurre and R.-R. Müller, Adaptive methods in BCI research-An introductory tutorial, in Brain-Computer Interfaces, eds. B. Allison, B. Graimann and G. Pfurtscheller, The Frontiers Collection (Springer, 2010), pp. 331-355.
36. D. J. McFarland, W. A. Sarnacki and J. R. Wolpaw, Should the parameters of a BCI translation algorithm be continually adapted? J. Neurosci. Methods 199(1) (2011) 103-107.
37. L. F. Nicolas-Alonso and J. Gomez-Gil, Brain computer interfaces, a review, Sensors 2 (2012) 1211-1279.
38. J. J. Vidal, Toward direct brain-computer communication, Ann. Rev. Biophys. Bioeng. 2 (1973) 157-180.
39. P. Shenoy, M. Krauledat, B. Blankertz, R. P. N. Rao and K.-R. Müller, Towards adaptive classification for BCI, J. Neural Eng. 3(1) (2006) R13-R23. (Pubitemid 43332376)
40. K.-R. Müller, M. Krauledat, G. Dornhege, G. Curio and B. Blankertz, Machine learning techniques for brain-computer interfaces, Biomed. Tech. 49(1) (2004) 11-22.
41. D. A. Peterson, J. N. Knight, M. J. Kirby, C. W. Anderson and M. H. Thaut, Feature selection and blind source separation in an EEG-based brain-computer interface, EURASIP J. Appl. Signal Process. 2005(19) (2005) 3128-3140. (Pubitemid 44796386)
42. M. Sabeti, R. Boostani, S. D. Katebi and G. W. Price, Selection of relevant features for EEG signal classification of schizophrenic patients, Biomed. Signal Process. Control 2(2) 2007 122-134. (Pubitemid 47109237)
43. I. Koprinska, Feature selection for brain-computer interfaces, in International Workshop on New Frontiers in Applied Data Mining (PA K D D ), Lecture Notes in Computer Science, Vol. 5669 (2009), pp. 106-117.
44. A. Cabrera, D. Farina and K. Dremstrup, Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery, Med. Biol. Eng. Comput. 48 (2010) 123-132.
45. R. Corralejo, R. Hornero and D. Alvarez, Feature selection using a genetic algorithm in a motor imagery-based brain computer interface, in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2011), pp. 7703-7706.
46. J. Faller, C. Vidaurre, T. Solis-Escalante, C. Neu-per and R. Scherer, Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI, IEEE Trans. Neural Syst. Rehabil. Eng. 20(3) (2012) 313-319.
47. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification (Wiley-Interscience, 2000).
48. B. Efron, T. Hastie, L. Johnstone and R. Tibshirani, Least angle regression, Ann. Stat. 32 (2004) 407-499. (Pubitemid 41250302)
49. A. Bartoli, On computing the prediction sum of squares statistic in linear least squares problems with multiple parameter or measurement sets, Int. J. Comput. Vis. 85 (2009) 133-142.
50. G. Rodríguez-Bermúdez, P. J. García-Laencina and J. Roca-González, A new approach for adaptive linear discrimination in brain computer interfaces, in 12th Int. Conf. Computational and Mathematical Methods in Science and Engineering (CMMSE 2012), Volume III, La Manga del Mar Menor, SPAIN, June (2012), pp. 1083-1086.
51. F. Lotte, M. Congedo, A. Ĺecuyer, F. Lamarche and B. Arnaldi, A review of classification algorithms for EEG-based brain-computer interfaces, J. Neural Eng. 4(2) (2007) R1-R13. (Pubitemid 46603810)
52. M. Tangermann, K.-R. Müller, Ad. Aertsen, N. Birbaumers, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. Miller, G. Müller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlögl, C. Vidaurre, S. Waldert and B. Blankertz, Review of the BCI competition IV, Front. Neurosci. 6(55) (2012) 1-29.
53. O. Faust, U. R. Acharya, L. C. Min and B. H. Sputh, Automatic identification of epileptic and back ground EEG signals using frequency domain parameters, Int. J. Neural Syst. 20(02) (2010) 159-176.
54. M. Cabrerizo, M. Ayala, M. Goryawala, P. Jayakar and M. Adjouadi, A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population, Int. J. Neural Syst. 22(02) (2012) 1250001.
55. U. R. Acharya, S. V. Sree and S. J. Suri, Automatic detection of epileptic EEG signals using higher order cumulant features, Int. J. Neural Syst. 21(05) (2011) 403-414.
56. M. Ahmadlou and H. Adeli, Wavelet-synchronization methodology: A new approach for EEG-based diagnosis of ADHD, Clin. EEG Neurosci. 41(1) (2010) 1-10.
57. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chaos-neural network methodology for eeg-based diagnosis of autistic spectrum disorder, J. Clin. Neurophysiol. 27(5) (2010) 328-333.
58. Z. Sankari, H. Adeli and A. Adeli, Wavelet coherence model for diagnosis of alzheimer's disease, Clin. EEG Neurosci. 43(3) (2012) 268-278.
59. S. Tong and N. V. Thankor, Quantitative EEG Analysis Methods and Clinical Applications (Artech House, 2009).
60. R. Palaniappan, Biological Signal Analysis (Ventus Publishing, 2010).
61. G. Pfurtscheller, C. Brunner, A. Schlgl and F. H. Lopes da Silva, Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks, NeuroImage 31(1) (2006) 153-159. (Pubitemid 43729000)
62. A. Vuckovic and F. Sepulveda, A two-stage four-class BCI based on imaginary movements of the left and the right wrist, Med. Eng. Phys. 34(7) (2012) 964-971.
63. B. Hjorth, EEG analysis based on time domain properties, Electroencephalogr. Clin. Neurophysiol. 29(3) (1970) 306-310.
64. C. W. Anderson, E. A. Stolz and S. Shamsunder, Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Trans. Biomed. Eng. 45(3) (1998) 277-286. (Pubitemid 28128653)
65. A. Schlögl, The Electroencephalogram and the Adaptive Autoregressive Model: Theory and Applications (Shaker Verlag, 2000).
66. G. Rodríguez-Bermúdez, J. Roca-González, F. Martínez-González, L. Pena-Mora, J. L. Roca-González and J. Roca-Dorda, Performance analysis of different feature-classifier binomials in motorimagering BCIs: Preliminary results, in 3rd Int. Symp. Applied Sciences in Biomedical and Communication Technologies, Rome, Italy, November (2010), pp. 1-5.
67. M. Billinger, C. Brunner and C. Neuper, Classification of adaptive autoregressive models at different sampling rates in a motor imagery-based BCI, in Fourth International BCI Meeting, Pacific Grove, CA, USA (2010).
68. N.-Y. Liang, P. Saratchandran, G.-B. Huang and N. Sundararajan, Classification of mental task from EEG signals using extreme learning machine, Int. J. Neural Syst. 16(01) (2006) 29-38. (Pubitemid 44099473)
69. C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein and G. Pfurtscheller, Rapid prototyping of an EEG-based brain-computer interface (BCI). IEEE Trans. Neural Sys. Rehabil. Eng. 9(1) (2001) 49-58. (Pubitemid 32721116)
70. J. F. Delgado-Saa and M. Cetin, Hidden conditional random fields for classification of imagery motor tasks from EEG data, in 19th European Signal Processing Conference (EUSIPCO 2011) Barcelona, SPAIN (2011), pp. 1377-1381.
71. W.-Y. Hsu, Continuous EEG signal analysis for asynchronous BCI application, Int. J. Neural Syst. 21(04) (2011) 335-350.
72. J. Fruitet, D. J. McFarland and J. R. Wol-paw, A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface, J. Neural Eng. 7(1) (2010) 16003.
73. K.-R. Muller, C. W. Anderson and G. E. Birch, Linear and nonlinear methods for brain-computer interfaces, IEEE Trans. Neural Syst. Rehabil. Eng. 11(2) (2003) 165-169. (Pubitemid 36935049)
74. C. Vidaurre and A. Schlogl, Comparison of adaptive features with linear discriminant classifier for brain computer interfaces, In 30th Annual Int. Conf. IEEE Proceedings of the Engineering in Medicine and Biology Society (EMBS) Vol. 1 (2008), pp. 173-176.
75. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection, IEEE Trans. Biomed. Eng. 54(9) (2007) 1545-1551. (Pubitemid 47301028)
76. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55(2) (2008) 512-518.
77. S. Ghosh-Dastidar and H. Adeli, Spiking neural networks, Int. J. Neural Syst. 19(4) (2009) 295-308.
78. K. Pauwels and M. M. Van Hulle, Head-centric disparity and epipolar geometry estimation from a population of binocular energy neurons, Int. J. Neural Syst. 22(3) (2012) 1250007-1250016.
79. N. M. Tomasevic, A. M. Neskovic and N. J. Neskovic, Neural network-based approach to EEG signal simulation, Int. J. Neural Syst. 22(3) (2012) 1250008-1250016.
80. U. R. Acharya, S. V. Sree, S. Chattopadhyay, W. Yu and P. C. A. Ang, Application of recurrence quantification analysis for the automated identification of epileptic EEG signals, Int. J. Neural Syst. 21(3) (2011) 199-211.
81. D. Krusienski, E. W. Sellers, F. Cabestaing, S. Bay-oudh, D. J. McFarland, T. M. Vaughan and J. R. Wolpaw, A comparison of classification techniques for the P300 speller, J. Neural Eng. 3(4) (2006) 299-305.
82. D. Serre, Matrices: Theory and Applications (Springer, New York, 2002).
83. I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3 (2003) 1157-1182.
84. F. Wilcoxon, Individual comparisons by ranking methods, Biometr. Bull. 1(6) (1945) 80-83.
85. A. Al-Shahib, R. Breitling and D. Gilbert, Franksum: New feature slection method for protein function prediction, Int. J. Neural Syst. 15(04) (2005) 259-275. (Pubitemid 44099494)
86. S. Lemm, B. Blankertz, T. Dickhaus and K.-R. Müller, Introduction to machine learning for brain imaging, NeuroImage 56 (2011) 387-399.
87. Y. Miche, M. VanHeeswijk, P. Bas, O. Simula and A. Lendasse, TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization, Neu-rocomputi ng 74(16) (2011) 2413-2421.
88. T.-K. Kim, B. Stenger, J. Kittler and R. Cipolla, Incremental linear discriminant analysis using sufficient spanning sets and its applications, Int. J. Com-put. Vis. 91 (2011) 216-232.