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

Abstract

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 b and c 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.

Evaluation of a Sound Quality Visual Feedback System for Bow Learning Technique in Violin Beginners: An EEG Study

“…However, in a recent study (Gutierrez and Ramírez-Moreno, 2016) changes in brain activity associated with the progression of the learning experience were estimated with different results. They monitored the process of learning to typewrite using the Colemark keyboard layout, which is an alternative to the QWERTY layout, finding a decreasing trend of the beta and gamma bands.…”

Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

“…We described the demographic and clinical characteristics in DPI, NDPI and HC subjects using analysis of variance (ANOVA) (Gutiérrez and Ramírez-Moreno, 2016) and an independent samples t-test.…”

Using Data Assimilation for Quantitative Electroencephalography Analysis

“…In both, the activities performed were related to cognitive skills. For future references, we will refer the dataset from [ 16 ] as LGR and the one from [ 17 ] as DM (related to the authors’ names of each dataset). In Table 1 , different characteristics of each dataset are described, such as the headset used for acquisition, its sampling rate, and the number of recordings.…”

“…All statistical tests were carried out with a 5% significance level. We were particularly interested in looking for significant differences in the PSD values at alpha, low- and high-beta, or gamma bands, given the previously reported findings in [ 16 , 17 ], respectively. Please note that a change in the frequency band was considered to be relevant when the majority of its PSD values (with 1 Hz resolution) turned out to have significant differences between the groups.…”

“…As described previously, this approach can be helpful to the experts of the area of neurosciences, like neurologists and academics, to make diagnosis and medical decisions related to patient performance. In this research, we focus our attention in showing the applicability of our proposed technique through the analysis of cognitive abilities that have been previously measured in two different datasets previously analyzed in [ 16 , 17 ]. Both datasets acquire QEEG data related to the development of a cognitive activity.…”