Skip to main content
Log in

Multiresolution Karhunen Loéve analysis of galvanic skin response for psycho-physiological studies

  • Published:
Metrika Aims and scope Submit manuscript

Abstract

This paper proposes the use of Multiresolution Karhunen Loéve (MR-KL) analysis to analyse Galvanic Skin Response (GSR) Signals. GSR signal, which is considered the golden standard in peripheral neuro-physiological and psycho-physiological studies, can be represented by two functions representing either the large and small scale variabilities of the series. The large scale variability, related to the tonic component, is modelled by smoothing splines while the small scale variability, related to the phasic component, is modelled by a MR-KL expansion. The recognition of these two components in a real data set is mandatory in neuro-psycho-physiology and we demonstrate the efficacy of the proposed methodology by analysing a pilot experiment on a group of 13 subjects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Balram N, Moura JMF (1993) Noncausal Gauss Markov random fields: parameter structure and estimation. IEEE Trans Inf Theory 39: 1333–1355

    Article  MATH  Google Scholar 

  • Buchel C, Dolan RJ (2000) Classical fear conditioning in functional neuroimaging. Curr Opin Neurobiol 10(2): 219–223

    Article  Google Scholar 

  • Craven P, Wahba G (1979) Smoothing noisy data with spline functions. Numer Math 31: 377–403

    Article  MathSciNet  MATH  Google Scholar 

  • Cressie N (1991) Statistics for spatial data. Wiley, New York

    MATH  Google Scholar 

  • De Boor C (2001) A practical guide to splines, revised edition. Springer, New York

    Google Scholar 

  • Di Battista T, Valentini P, Di Romualdo S (2007) Functional data analysis of GSR signal. In: S.Co. 2007. Complex models and computational intensive methods for estimation and prediction, pp 169–174

  • Ekman P, Levenson RW, Friesen WV (1983) Autonomic nervous system activity distinguishes among emotions. Science 221(4616): 1208–1210

    Article  Google Scholar 

  • Fontanella L, Ippoliti L, Mardia K (2005) Exploring spatio-temporal variability by Eigen-decomposition techniques. Invited paper at the conference in statistics and environment. Meeting of the Italian Statistical Society, 21–23 September 2005, Messina (Italy)

  • Huang HC, Cressie N (2000) Deterministic/stochastic wavelet decomposition for recovery of signal from noisy data. Technometrics 42: 262–276

    Article  MathSciNet  MATH  Google Scholar 

  • Ippoliti L, Romagnoli L, Fontanella L (2005) A noise estimation method for corrupted correlated data. Stat Methods Appl 14(3): 343–356

    Article  MathSciNet  MATH  Google Scholar 

  • Kakarala R, Ogunbona PO (2001) Signal analysis using a multiresolution form of the singular value decomposition. IEEE Trans Image Process 10(5): 724–735

    Article  MathSciNet  MATH  Google Scholar 

  • Karhunen K (1947) Uber linear Methoden in der Wahrscheinlichkeitsrechnung. Ann Acad Sci Fenn (AI) 37: 1–79

    Google Scholar 

  • Kostantinides K, Yao K (1988) Statistical analysis of effective singular values in matrix rank determination. IEEE Trans Acoust Speech Signal Process 36: 757–763

    Article  Google Scholar 

  • Kotzé HF, Möller AT (1990) Effect of auditory subliminal stimulation on GSR. Psychol Rep 67(3): 931–934

    Article  Google Scholar 

  • Learned RE, Willsky AS (1995) A wavelet packet approach to transient signal classification. Appl Comput Harmon Anal 2: 256–278

    Article  Google Scholar 

  • Liavas AP, Regalia PA, Delmas JP (1999) Blind channel approximation: effective chanel order determination. IEEE Trans Signal Process 47: 3336–3344

    Article  Google Scholar 

  • Lim CL, Rennie C, Barry RJ, Bahramali H, Lazzaro I, Manor B, Gordon E (1997) Decomposing skin conductance into tonic and phasic components. Int J Psychophysiol 25(2): 97–109

    Article  Google Scholar 

  • Lisetti CL, Nasoz F (2004) Using non-invasive wearable computers to recognize human emotions from physiological signal. J Appl Signal Process 48(11): 1672–1687

    Article  Google Scholar 

  • Loève M (1945) Functions Aleatoires de Second Ordre. CR Acad Sci Paris 220: 469

    Google Scholar 

  • Macedonio MF, Parsons TD, Di Giuseppe RA, Weiderhold BK, Rizzo AA (2007) Immersiveness and physiological arousal within panoramic video-based virtual reality. Cyberpsychol Behav 10(4): 508–515

    Article  Google Scholar 

  • Mallat S (1998) A wavelet tour of signal processing. Academic Press, New York

    MATH  Google Scholar 

  • Merla A, Di Donato L, Rossini PM, Romani GL (2004) Emotion detection through functional infrared imaging: preliminary results. Biomed Tech 48(2): 284–286

    Google Scholar 

  • Ramsay JO, Silverman BW (2005) Functional data analysis, 2nd edn. Springer, New York

    Google Scholar 

  • Shastri D, Merla A, Tsiamyrtzis P, Pavlidis I (2009) Imaging facial signs of neurophysiological responses. IEEE Trans Biomed Eng 56(2): 477–484

    Article  Google Scholar 

  • Silverman BW (1985) Some aspects of the spline smoothing approach to non-parametric regression curve fitting. J R Stat Soc Series B 47: 1–52 (with discussion)

    MATH  Google Scholar 

  • Stone M (1974) Cross-validatory choice and assesment of statistical predictions. J R Stat Soc Series B 36: 111–147 (with discussion)

    MATH  Google Scholar 

  • Tarvainen MP, Karjalainen PA, Koistenen AS, Valkonen-Korhonen M (2000) Principal component analysis of galvanic skin response. J Appl Signal Process 4: 3011–3014

    Google Scholar 

  • Tarvainen MP, Koistinen AS, Valkonen-Korhonen M, Partanen J, Karjalainen PA (2001) Analysis of galvanic skin responses with principal components and clustering techniques. IEEE Trans Biomed Eng 48: 1071–1079

    Article  Google Scholar 

  • Unser M (1993) An extension of the Karhunen-Loéve transform for wavelets and perfect reconstruction filterbanks. Math Imaging SPIE 2034: 45–56

    Google Scholar 

  • VanderArk SD, Ely D (1992) Biochemical and galvanic skin responses to music stimuli by college students in biology and music. Percept Mot Skills 74(3): 1079–1090

    Article  Google Scholar 

  • Vecchiato G, Astolfi L, DeVico Fallani F, Cincotti F, Mattia D, Salinari S, Soranzo R, Babiloni F (2010) Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain Topogr 23(2): 165–179

    Article  Google Scholar 

  • Walczak B, van den Bogaert B, Massart DL (1996) Application of wavelet packet transform in pattern recognition of near-IR data. Anal Chem 68(10): 1742–1747

    Article  Google Scholar 

  • Walker JS (1999) A primer on wavelets and their scientific applications. Chapman and Hall, CRC, Boca Raton

    Book  MATH  Google Scholar 

  • Zhai J, Barreto A (2006) Stress detection in computer users through non-invasive monitoring of physiological signals. Biomed Sci Instrum 42: 495–500

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luigi Ippoliti.

Additional information

ITAB: Institute of Advanced Biomedical Technologies, Foundation University G. d’Annunzio.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fontanella, L., Ippoliti, L. & Merla, A. Multiresolution Karhunen Loéve analysis of galvanic skin response for psycho-physiological studies. Metrika 75, 287–309 (2012). https://doi.org/10.1007/s00184-010-0327-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00184-010-0327-3

Keywords

Navigation