Benefits of functional PCA in the analysis of single-trial auditory evoked potentials

Evoked potentials reflect neural processing and are widely used to studying sensory perception. Here we applied a functional approach to studying single-trial auditory evoked potentials in the rat model of tinnitus, in which overdoses of salicylate are known to alter sound perception characteristically. Single-trial evoked potential integrals were generated with sound stimuli (tones and clicks) presented systematically over an intensity range and further assessed using the functional principal component analysis. Comparisons between the single-trial responses for each sound type and each treatment were done by inspecting the scores corresponding to the first two principal components. An analogous analysis was performed on the first derivative of the response functions. We conclude that the functional principal component analysis is capable of differentiating between the controls and salicylate treatments for each type of sound. It also well separates the response function for tones and clicks. The results of linear discriminant analysis show, that scores of the first two principal components are effective cluster predictors. However, the distinction is less pronounced in case the first derivative of the response.

[1]  G. Wahba Smoothing noisy data with spline functions , 1975 .

[2]  James O. Ramsay,et al.  Applied Functional Data Analysis: Methods and Case Studies , 2002 .

[3]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[4]  Gavin M. Bidelman,et al.  Amplified induced neural oscillatory activity predicts musicians’ benefits in categorical speech perception , 2017, Neuroscience.

[5]  F. Ferraty,et al.  The Oxford Handbook of Functional Data Analysis , 2011, Oxford Handbooks Online.

[6]  Petr Lánský,et al.  Altered intensity coding in the salicylate-overdose animal model of tinnitus , 2015, Biosyst..

[7]  P. Vieu,et al.  Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics) , 2006 .

[8]  A. Cuevas A partial overview of the theory of statistics with functional data , 2014 .

[9]  Y. Cazals Auditory sensori-neural alterations induced by salicylate , 2000, Progress in Neurobiology.

[10]  J. Eggermont,et al.  Ringing Ears: The Neuroscience of Tinnitus , 2010, The Journal of Neuroscience.

[11]  Piotr Kokoszka,et al.  Inference for Functional Data with Applications , 2012 .

[12]  K Alho,et al.  Selective attention in auditory processing as reflected by event-related brain potentials. , 1992, Psychophysiology.

[13]  Hans-Georg Müller Functional Data Analysis. , 2011 .

[14]  Wei Qiu,et al.  Functional data analysis of single-trial auditory evoked potentials recorded in the awake rat , 2017, Biosyst..

[15]  Aldo Goia,et al.  An introduction to recent advances in high/infinite dimensional statistics , 2016, J. Multivar. Anal..

[16]  Peter Craven,et al.  Smoothing noisy data with spline functions , 1978 .

[17]  Wenqing Liu,et al.  Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials , 2006, IEEE Transactions on Biomedical Engineering.