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A Nonlinear Technique for Analysis of Big Data in Neuroscience

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Big Data Analytics

Abstract

Recent technological advances have paved the way for big data analysis in the field of neuroscience. Machine learning techniques can be used effectively to explore the relationship between large-scale neural and behavorial data. In this chapter, we present a computationally efficient nonlinear technique which can be used for big data analysis. We demonstrate the efficacy of our method in the context of brain computer interface. Our technique is piecewise linear and computationally inexpensive and can be used as an analysis tool to explore any generic big data.

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References

  1. Focus on big data. Nat Neurosci 17(11):1429 http://www.nature.com/neuro/journal/v17/n11/full/nn.3856.html

  2. Ang KK, Chin ZY, Zhang H, Guan C (2008) Filter bank common spatial pattern (fbcsp) in brain-computer interface. In: IEEE International joint conference on neural networks, 2008. IJCNN 2008 (IEEE World congress on computational intelligence). IEEE, pp 2390–2397

    Google Scholar 

  3. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal 19(7):711–720

    Google Scholar 

  4. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kbler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398(6725):297–298

    Article  Google Scholar 

  5. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller K-R (2008) Optimizing spatial filters for robust eeg single-trial analysis. IEEE Signal Process Mag 25(1):41–56

    Article  Google Scholar 

  6. Chen L-F, Liao H-YM, Ko M-T, Lin J-C, Yu G-J (2000) A new lda-based face recognition system which can solve the small sample size problem. Pattern Recogn 33(10):1713–1726

    Article  Google Scholar 

  7. Cover TM, Campenhout JMV (1977) On the possible ordering in the measurement selection problem. IEEE Trans Syst Man Cybern 7:657–661

    Article  MATH  Google Scholar 

  8. Daly JJ, Cheng R, Rogers J, Litinas K, Hrovat K, Dohring M (2009) Feasibility of a new application of noninvasive brain computer interface (bci): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 33(4):203–211

    Google Scholar 

  9. Daniels MJ, Kass RE (2001) Shrinkage estimators for covariance matrices. Biometrics 57:1173–1184

    Article  MathSciNet  MATH  Google Scholar 

  10. Das K, Meyer J, Nenadic Z (2006) Analysis of large-scale brain data for brain-computer interfaces. In: Proceedings of the 28th Annual international conference of the IEEE engineering in medicine and biology society, pp 5731–5734

    Google Scholar 

  11. Das K, Nenadic Z (2009) An efficient discriminant-based solution for small sample size problem. Pattern Recogn 42(5):857–866

    Article  MATH  Google Scholar 

  12. Das K, Osechinskiy S, Nenadic Z (2007) A classwise pca-based recognition of neural data for brain-computer interfaces. In: Proceedings of the 29th Annual international conference of the IEEE engineering in medicine and biology society, pp 6519–6522

    Google Scholar 

  13. Das K, Rizzuto D, Nenadic Z (2009) Estimating mental states for brain-computer interface. IEEE Trans Biomed Eng 56(8):2114–2122

    Article  Google Scholar 

  14. Do AH, Wang PT, King CE, Chun SN, Nenadic Z (2013) Brain-computer interface controlled robotic gait orthosis. J Neuro Eng Rehabil 10(111)

    Google Scholar 

  15. Do AH, Wang PT, King CE, Schombs A, Cramer SC, Nenadic Z (2012) Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke. Conf Proc IEEE Eng Med Biol Soc 6414–6417:2012

    Google Scholar 

  16. Duda RO, Hart PE, Stork DG(2001) Pattern classification. Wiley-Interscience

    Google Scholar 

  17. Enke H, Partl A, Reinefeld A, Schintke F (2012) Handling big data in astronomy and astrophysics: rich structured queries on replicated cloud data with xtreemfs. Datenbank-Spektrum 12(3):173–181

    Article  Google Scholar 

  18. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188

    Article  Google Scholar 

  19. Fukunaga K (1990) Inroduction to statistical pattern recognition, 2nd edn. Academic Press

    Google Scholar 

  20. He X, Yan S, Hu Y, Niyogi P (2005) Face recognition using laplacian faces. IEEE Trans Pattern Anal 27(3):328–340

    Article  Google Scholar 

  21. Hochberg L, Serruya M, Friehs G, Mukand J, Saleh M, Caplan A, Branner A, Chen D, Penn RD, Donoghue J (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164–171

    Google Scholar 

  22. Hoffbeck JP, Landgrebe DA (1996) Covariance matrix estimation and classification with limited training data. IEEE Trans Pattern Anal 18(7):763–767

    Article  Google Scholar 

  23. Huang R, Liu Q, Lu H, Ma S (2002) Solving the small sample size problem of lda. In: ICPR ’02: Proceedings of the 16th International conference on pattern recognition (ICPR’02), vol 3. IEEE Computer Society, p 30029

    Google Scholar 

  24. Huber PJ (1985) Projection pursuit. Ann Stat 13(2):435–475

    Article  MathSciNet  MATH  Google Scholar 

  25. Insel TR, Landis SC, Collins FS (2013) The nih brain initiative. Science 340(6133):687–688

    Article  Google Scholar 

  26. Kawanabe M, Samek W, Müller K-R, Vidaurre C (2014) Robust common spatial filters with a maxmin approach. Neural Comput 26(2):349–376

    Article  MathSciNet  Google Scholar 

  27. Kesner RP, Farnsworth G, Kametani H (1991) Role of parietal cortex and hippocampus in representing spatial information. Cereb Cortex 1(5):367–373

    Article  Google Scholar 

  28. King CE, Dave KR, Wang PT, Mizuta M, Reinkensmeyer DJ, Do AH, Moromugi S, Nenadic Z (2014) Performance assessment of a brain-computer interface driven hand orthosis. Annals of Biomed Eng 42(10):2095–2105

    Article  Google Scholar 

  29. King CE, Wang PT, McCrimmon CM, Chou CCY, Do AH, Nenadic Z (2014) Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant. In: Proceedings of the 36th Annual international conference IEEE engineering and medical biological society, pp 1238–1242

    Google Scholar 

  30. Kirby M, Sirovich L (1990) Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal 12:103–108

    Article  Google Scholar 

  31. Kittler J (1978) Feature set search algorithms. In: Chen CH (ed) Pattern recognition and signal processing. Sijthoff and Noordhoff, Alphen aan den Rijn, The Netherlands, pp 41–60

    Google Scholar 

  32. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Joint commission international, pp 1137–1145

    Google Scholar 

  33. Leuthardt E, Schalk G, Wolpaw J, Ojemann J, Moran D (2004) A brain-computer interface using electrocorticographic signals in humans. J Neural Eng 1(2):63–71

    Google Scholar 

  34. Lewis S, Csordas A, Killcoyne S, Hermjakob H, Hoopmann MR, Moritz RL, Deutsch EW, Boyle J (2012) Hydra: a scalable proteomic search engine which utilizes the hadoop distributed computing framework. BMC Bioinform 13(1):324

    Article  Google Scholar 

  35. Loog M, Duin R (2004) Linear dimensionality reduction via a heteroscedastic extension of lda: the chernoff criterion. IEEE Trans Pattern Anal 26:732–739

    Article  Google Scholar 

  36. Markram H (2012) The human brain project. Sci Am 306(6):50–55

    Article  Google Scholar 

  37. Mathé C, Sagot M-F, Schiex T, Rouze P (2002) Current methods of gene prediction, their strengths and weaknesses. Nucleic Acids Res 30(19):4103–4117

    Article  Google Scholar 

  38. McCrimmon CM, King CE, Wang PT, Cramer SC, Nenadic Z, Do AH (2014) Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors. Conf Proc IEEE Eng Med Biol Soc 2014:1247–1250

    Google Scholar 

  39. McFarland DJ, McCane LM, David SV, Wolpaw JR (1997) Spatial filter selection for EEG-based communication. Electroeng Clin Neuro 103(3):386–394

    Article  Google Scholar 

  40. Müller-Gerking J, Pfurtscheller G, Flyvbjerg H (1999) Designing optimal spatial filters for single-trial eeg classification in a movement task. Clin Neurophysiol 110(5):787–798

    Article  Google Scholar 

  41. Musallam S, Corneil BD, Greger B, Scherberger H, Andersen RA (2004) Cognitive control signals for neural prosthetics. Science 305(5681):258–262

    Article  Google Scholar 

  42. Nenadic Z (2007) Information discriminant analysis: Feature extraction with an information-theoretic objective. IEEE Trans Pattern Anal 29(8):1394–1407

    Article  Google Scholar 

  43. Nenadic Z, Rizzuto D, Andersen R, Burdick J (2007) Advances in cognitive neural prosthesis: recognition of neural data with an information-theoretic objective. In: Dornhege G, Millan J, Hinterberger T, McFarland D, Muller KR (eds) Toward brain computer interfacing, Chap 11. The MIT Press, pp 175–190

    Google Scholar 

  44. Nilsson T, Mann M, Aebersold R, Yates JR III, Bairoch A, Bergeron JJ (2010) Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods 7(9):681–685

    Article  Google Scholar 

  45. ODriscoll A, Daugelaite J, Sleator RD (2013) Big data, hadoop and cloud computing in genomics. J Biomed Inf 46(5):774–781

    Google Scholar 

  46. Parzen E (1962) On the estimation of a probability density function and mode. Ann Math Stat 33:1065–1076

    Article  MathSciNet  MATH  Google Scholar 

  47. Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroeng Clin Neuro 103(6):642–651

    Article  Google Scholar 

  48. Pfurtscheller G, Neuper C, Muller GR, Obermaier B, Krausz G, Schlogl A, Scherer R, Graimann B, Keinrath C, Skliris D, Wortz GSM, Schrank C (2003) Graz-BCI: state of the art and clinical applications. IEEE Trans Neural Syst Rehabil 11(2):177–180

    Article  Google Scholar 

  49. Ramos-Murguialday A, Broetz D, Rea M, Ler L, Yilmaz O, Brasil FL, Liberati G, Curado MR, Garcia-Cossio E, Vyziotis A, Cho W, Agostini M, Soares E, Soekadar S, Caria A, Cohen LG, Birbaumer N (2013) Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 74(1):100–108

    Google Scholar 

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

    Article  Google Scholar 

  51. Rao CR (1948) The utilization of multiple measurements in problems of biological classification. J R Stat Soc B Methods 10(2):159–203

    MathSciNet  MATH  Google Scholar 

  52. Rizzuto D, Mamelak A, Sutherling W, Fineman I, Andersen R (2005) Spatial selectivity in human ventrolateral prefrontal cortex. Nat Neurosci 8:415–417

    Google Scholar 

  53. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

  54. Santhanam G, Ryu SI, Yu1 BM, Afshar A, Shenoy KV (2006) A high-performance brain-computer interface. Nature 442:195–198

    Google Scholar 

  55. Saon G, Padmanabhan M (2000) Minimum Bayes error feature selection for continuous speech recognition. In: NIPS, pp 800–806

    Google Scholar 

  56. Schäfer J, Strimmer K (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 4(1):Article 32

    Google Scholar 

  57. Schölkopf B, Smola A, Müller KR (1997) Kernel principal component analysis. In: Artificial neural networks ICANN’97. Springer, pp 583–588

    Google Scholar 

  58. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP (2002) Instant neural control of a movement signal. Nature 416(6877):141–142

    Article  MATH  Google Scholar 

  59. Shenoy P, Miller K, Ojemann J, Rao R (2007) Finger movement classification for an electrocorticographic bci. In: 3rd International IEEE/EMBS conference on neural engineering, 2007. CNE ’07, pp 192–195, 2–5 May 2007

    Google Scholar 

  60. Shenoy P, Miller K, Ojemann J, Rao R (2008) Generalized features for electrocorticographic bcis. IEEE Trans Biomed Eng 55(1):273–280

    Article  Google Scholar 

  61. Shenoy P, Rao R (2004) Dynamic bayes networks for brain-computer interfacing. In: Advances in neural information processing systems, pp 1265–1272

    Google Scholar 

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

    Article  Google Scholar 

  63. Thomaz CE, Gillies DF, Feitosa RQ (2001) Small sample problem in bayes plug-in classifier for image recognition. In: International conference on image and vision computing, New Zealand, pp 295–300

    Google Scholar 

  64. Thorpe S, Fize D, Marlot C (1995) Speed of processing in the human visual system. Nature 381(6582):520–522

    Article  Google Scholar 

  65. Torkkola K (2002) Discriminative features for document classification. In: Proceedings 16th international conference on pattern recognition, vol 1, pp 472–475

    Google Scholar 

  66. Townsend G, Graimann B, Pfurtscheller G (2006) A comparison of common spatial patterns with complex band power features in a four-class bci experiment. IEEE Trans Biomed Eng 53(4):642–651

    Google Scholar 

  67. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71–86

    Google Scholar 

  68. Wang W, Collinger JL, Degenhart AD, Tyler-Kabara EC, Schwartz AB, Moran DW, Weber DJ, Wodlinger B, Vinjamuri RK, Ashmore RC et al (2013) An electrocorticographic brain interface in an individual with tetraplegia. PloS ONE 8(2):e55344

    Article  Google Scholar 

  69. Wang X, Tang X (2004) Dual-space linear discriminant analysis for face recognition. In: 2004 IEEE Computer society conference on computer vision and pattern recognition (CVPR’04), vol 02, pp 564–569, 2004

    Google Scholar 

  70. Wang Y, Gao S, Gao X (2006) Common spatial pattern method for channel selelction in motor imagery based brain-computer interface. In: 27th Annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005. IEEE, pp 5392–5395

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  73. Wolpaw J, Wolpaw EW (2011) Brain-computer interfaces: principles and practice. Oxford University Press

    Google Scholar 

  74. Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 101(51):17849–17854

    Article  Google Scholar 

  75. Yang J, Frangi AF, Yang J-Y, Zhang D, Jin Z (2005) Kpca plus lda: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  76. Yu H, Yang H (2001) A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recogn Lett 34(10):2067–2070

    Article  MATH  Google Scholar 

  77. Zhang S, Sim T (2007) Discriminant subspace analysis: a fukunaga-koontz approach. IEEE Trans Pattern Anal 29(10):1732–1745

    Google Scholar 

  78. Zhu M, Martinez AM (2006) Subclass discriminant analysis. IEEE Trans Pattern Anal 28(8):1274–1286

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics and Statistics, IISER Kolkata, Mohanpur, 741246, India

    Koel Das

  2. Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA

    Zoran Nenadic

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  1. Koel Das
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  2. Zoran Nenadic
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Correspondence to Koel Das .

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Editors and Affiliations

  1. Indian Institute of Public Health , Hyderabad, India

    Saumyadipta Pyne

  2. CRRao AIMSCS, University of Hyderabad Campus CRRao AIMSCS, Hyderabad, India

    B.L.S. Prakasa Rao

  3. CRRao AIMSCS, University of Hyderabad Campus CRRao AIMSCS, Hyderabad, India

    S.B. Rao

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Das, K., Nenadic, Z. (2016). A Nonlinear Technique for Analysis of Big Data in Neuroscience. In: Pyne, S., Rao, B., Rao, S. (eds) Big Data Analytics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3628-3_13

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