Cognitive Neurodynamics

, Volume 10, Issue 2, pp 175–183 | Cite as

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities

  • David GutiérrezEmail author
  • Mauricio A. Ramírez-Moreno
Brief Communication


We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of \(\beta\) and \(\gamma\) rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.


Neurocognitive phenomics Electroencephalography Brain rhythms Power spectral density Functional ANOVA Learning 



This work was supported by the Mexican Council of Science and Technology (Conacyt) through Grant 220145.


  1. Berka C, Levendowski D, Westbrook P, Davis G, Johnson R, Popovic D (2010) Interactive psychophysiological profiler method and system. US Patent App. 12/466,263Google Scholar
  2. Canolty RT, Knight RT (2010) The functional role of cross-frequency coupling. Trends Cogn Sci 14(11):506–515CrossRefPubMedPubMedCentralGoogle Scholar
  3. Chaumon M, Schwartz D, Tallon-Baudry C (2009) Unconscious learning versus visual perception: dissociable roles for gamma oscillations revealed in MEG. J Cogn Neurosci 21(12):2287–2299CrossRefPubMedGoogle Scholar
  4. Dolce G, Waldeier H (1974) Spectral and multivariate analysis of EEG changes during mental activity in man. Electroencephalogr Clin Neurophysiol 36:577–584CrossRefPubMedGoogle Scholar
  5. Duch W (2013) Brains and education: towards neurocognitive phenomics. In: Reynolds N, Webb M, Sysło MM, Dagiene V (eds) Learning while we are connected, vol 3. Citeseer, pp 12–23Google Scholar
  6. Egner T, Gruzelier JH (2001) Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport 12(18):4155–4159CrossRefPubMedGoogle Scholar
  7. Erbil N, Ungan P (2007) Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements. Brain Res 1169:44–56CrossRefPubMedGoogle Scholar
  8. Fujikoshi Y (1993) Two-way ANOVA models with unbalanced data. Discrete Math 116(13):315–334CrossRefGoogle Scholar
  9. Gruber T, Mller MM (2005) Oscillatory brain activity dissociates between associative stimulus content in a repetition priming task in the human EEG. Cereb Cortex 15(1):109–116CrossRefPubMedGoogle Scholar
  10. Gruber T, Müller MM, Keil A (2002) Modulation of induced gamma band responses in a perceptual learning task in the human EEG. J Cogn Neurosci 14(5):732–744CrossRefPubMedGoogle Scholar
  11. Gutiérrez D (2013) Multivariate time-varying autoregressive modeling of fetal sympatho-vagal balance through gestation. Biomed Eng Appl Basis Commun 25(01):1350014CrossRefGoogle Scholar
  12. Gutiérrez D, Ramírez-Moreno MA, Lazcano-Herrera AG (2015) Assessing the acquisition of a new skill with electroencephalography. In: Proceedings of the 7th annual international IEEE EMBS conference on neural engineering. IEEE, pp 727–730Google Scholar
  13. Harmony T, Hinojosa G, Marosi E, Becker J, Rodriguez M, Reyes A, Rocha C (1990) Correlation between EEG spectral parameters and an educational evaluation. Int J Neurosci 54(1–2):147–155CrossRefPubMedGoogle Scholar
  14. Johnson RR, Popovic DP, Olmstead RE, Stikic M, Levendowski DJ, Berka C (2011) Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model. Biol Psychol 87(2):241–250CrossRefPubMedPubMedCentralGoogle Scholar
  15. Miltner WHR, Braun C, Arnold M, Witte H, Taub E (1999) Coherence of gamma-band EEG activity as a basis for associative learning. Nat Lett 397:434–436CrossRefGoogle Scholar
  16. Niemann J, Winker T, Gerling J, Landwehrmeyer B, Jung R (1991) Changes of slow cortical negative dc-potentials during the acquisition of a complex finger motor task. Exp Brain Res 85(2):417–422CrossRefPubMedGoogle Scholar
  17. Ramsay JO (2006) Functional data analysis. Wiley Online LibraryGoogle Scholar
  18. Salazar-Varas R, Gutiérrez D (2015) An optimized feature selection and classification method for using electroencephalographic coherence in brain–computer interfaces. Biomed Signal Process Control 18:11–18CrossRefGoogle Scholar
  19. Schneider T, Neumaier A (2001) Algorithm 808: ARfit—a Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw (TOMS) 27(1):58–65CrossRefGoogle Scholar
  20. Thompson LW, Obrist WD (1964) EEG correlates of verbal learning and overlearning. Electroencephalogr Clin Neurophysiol 16:332–342CrossRefPubMedGoogle Scholar
  21. Walter DO (1963) Spectral analysis for electroencephalograms: mathematical determination of neurophysiological relationships from records of limited duration. Exp Neurol 8(2):155–181CrossRefPubMedGoogle Scholar
  22. Yucha C, Montgomery D (2008) Evidence-based practice in biofeedback and neurofeedback. AAPB, Wheat RidgeGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Center for Research and Advanced Studies (Cinvestav), Monterrey’s UnitApodacaMexico

Personalised recommendations