The Comparison between Visually and Auditory Oddball Tasks in the EEG Experiment with Healthy Subjects
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Abstract
Purpose: The purpose of this study is estimating and comparing the three different dimensions of the EEG and studying the trials variability for two auditory and visually oddball tasks in the healthy subjects. They include regional as the region of the brain, longitudinal as the repetition of the stimuli, and functional as whole curve of Evoked Related Potential (ERP), dimensions.
Materials and Methods: The sample size is seventeen, with six females, in this three-trial study with standard and target stimuli per task. The dataset was downloaded from the internet and preprocessed. The Hybrid Principal Component Analysis (HPCA) decomposed the ERPs and estimated eigen components of three dimensions. The 95% Bayesian credible sets and trial effects as random effects of the first eigen component of each dimensions studied with the Generalized Additive Mixed Model (GAMM).
Results: The p-values of the interaction effects between time and stimuli, repeats and stimuli and regions and stimuli are <0.05 for three dimensions, except in auditory task of longitudinal dimension and in visual task of regional dimension that are >0.05. The p-value of trial effects are <0.05 and for auditory task in the longitudinal dimension is borderline.
Conclusion: The HPCA methodology decompose the time-domain ERPs to the functional-longitudinal and regional dimensions. The first eigencompments capture the most variations of every dimensions and we study the behavior of three-dimensions with them. We conclude that the repeating of the stimuli has a positive effect on the visual tasks. We also study the variability between trials with GAMM that are statistically significant.
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2- J. Ramsey, B. W. Silverman, "Functional Data Analysis," Springer, 2005.
3- P. A. Valdes-Sosa, "Spatio-temporal autoregressive models defined over brain manifolds," (in eng), Neuroinformatics, vol. 2, no. 2, pp. 239-50, 2004, doi: 10.1385/ni:2:2:239.
4- C. M. Crainiceanu and A. J. Goldsmith, "Bayesian functional data analysis using WinBUGS," Journal of statistical software, vol. 32, no. 11, 2010.
5- Z. Lin, L. Wang, and J. Cao, "Interpretable functional principal component analysis," (in eng), Biometrics, vol. 72, no. 3, pp. 846-54, Sep 2016, doi: 10.1111/biom.12457.
6- K. Hasenstab et al., "A multi-dimensional functional principal components analysis of EEG data," (in eng), Biometrics, vol. 73, no. 3, pp. 999-1009, Sep 2017, doi: 10.1111/biom.12635.
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9- J. P. Rosenfeld, "P300 in detecting concealed information and deception: A review," Psychophysiology, vol. 57, no. 7, p. e13362, 2020.
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11- G. Hajcak and D. Foti, "Significance?... Significance! Empirical, methodological, and theoretical connections between the late positive potential and P300 as neural responses to stimulus significance: An integrative review," Psychophysiology, p. e13570, 2020.
12- E. M. Bernat, J. S. Ellis, M. D. Bachman, and B. M. Hicks, "P3 amplitude reductions are associated with shared variance between internalizing and externalizing psychopathology," Psychophysiology, p. e13618, 2020.
13- Y. M. Fonken, J. W. Kam, and R. T. Knight, "A differential role for human hippocampus in novelty and contextual processing: Implications for P300," Psychophysiology, p. e13400, 2019.
14- S. C. Kao et al., "A systematic review of physical activity and cardiorespiratory fitness on P3b," Psychophysiology, vol. 57, no. 7, p. e13425, 2020.
15- M. Leckey and K. D. Federmeier, "The P3b and P600 (s): Positive contributions to language comprehension," Psychophysiology, vol. 57, no. 7, p. e13351, 2020.
16- T. Riggins and L. S. Scott, "P300 development from infancy to adolescence," Psychophysiology, vol. 57, no. 7, p. e13346, 2020.
17- B. Z. Allison, A. Kübler, and J. Jin, "30+ years of P300 brain–computer interfaces," Psychophysiology, vol. 57, no. 7, p. e13569, 2020.
18- V. Salmela, E. Salo, J. Salmi, and K. Alho, "Spatiotemporal dynamics of attention networks revealed by representational similarity analysis of EEG and fMRI," Cerebral Cortex, vol. 28, no. 2, pp. 549-560, 2018.
19- B. R. Conroy, J. M. Walz, B. Cheung, and P. Sajda, "Fast simultaneous training of generalized linear models (FaSTGLZ)," arXiv preprint arXiv:1307.8430, 2013.
20- B. R. Conroy, J. M. Walz, and P. Sajda, "Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate fMRI decoding models," PloS one, vol. 8, no. 11, 2013.
21- J. M. Walz, R. I. Goldman, M. Carapezza, J. Muraskin, T. R. Brown, and P. Sajda, "Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem," Journal of Neuroscience, vol. 33, no. 49, pp. 19212-19222, 2013.
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23- J. M. Walz, R. I. Goldman, M. Carapezza, J. Muraskin, T. R. Brown, and P. Sajda, "Prestimulus EEG alpha oscillations modulate task-related fMRI BOLD responses to auditory stimuli," NeuroImage, vol. 113, pp. 153-163, 2015.
24- "https://openneuro.org/." [Online]. Available: https://openneuro.org/.
25- R. C. T. (2020), " R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria." [Online]. Available: https://www.R-project.org/.
26- A. Delorme and S. Makeig, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis," Journal of neuroscience methods, vol. 134, no. 1, pp. 9-21, 2004.
27- J. Harezlak, D. Ruppert, and M. P. Wand, Semiparametric regression with R. Springer, 2018.
28- S. D. Team, "RStan: the R Interface to Stan. R package version 2.17. 3," ed, 2018.
29- S. Wood and M. S. Wood, "Package ‘mgcv’," R package version, vol. 1, p. 29, 2015.
30- J. I. Padilla-Buritica, J. V. Hurtado, and G. Castellanos-Dominguez, "Supervised piecewise network connectivity analysis for enhanced confidence of auditory oddball tasks," Biomedical Signal Processing and Control, vol. 52, pp. 341-346, 2019.
31- A. Modirshanechi, M. M. Kiani, and H. Aghajan, "Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks," NeuroImage, vol. 196, pp. 302-317, 2019.
32- A. S. Dizaji and H. Soltanian-Zadeh, "A change-point analysis method for single-trial study of simultaneous EEG-fMRI of auditory/visual oddball task," bioRxiv, p. 100487, 2017.
33- L. Hong, J. M. Walz, and P. Sajda, "Your eyes give you away: prestimulus changes in pupil diameter correlate with poststimulus task-related EEG dynamics," PLoS One, vol. 9, no. 3, p. e91321, 2014.
34- M. A. A. Turkman, C. D. Paulino, and P. Müller, Computational Bayesian Statistics: An Introduction. Cambridge University Press, 2019.
35- X. Qi and R. Luo, "Function-on-function regression with thousands of predictive curves," Journal of Multivariate Analysis, vol. 163, pp. 51-66, 2018.
36- K. Hasenstab, C. A. Sugar, D. Telesca, K. McEvoy, S. Jeste, and D. Şentürk, "Identifying longitudinal trends within EEG experiments," Biometrics, vol. 71, no. 4, pp. 1090-1100, 2015.
37- E. Campos et al., "Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes," NeuroImage, vol. 212, p. 116630, 2020.
| Files | ||
| Issue | Vol 7 No 4 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v7i4.5322 | |
| Keywords | ||
| Electroencephalography Functional Data Analysis Bayesian Data Analysis Attention Evoked Related Potential | ||
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How to Cite
1.
Fayaz M, Abadi A, Khodakarim S. The Comparison between Visually and Auditory Oddball Tasks in the EEG Experiment with Healthy Subjects. Frontiers Biomed Technol. 2020;7(4):249-258.
