Assessing the acquisition of a new skill with electroencephalography

Gueorguiev, David; Ramírez-Moreno, Mauricio A.; Lazcano-Herrera, Alicia Guadalupe

doi:10.1109/ner.2015.7146726

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…In all cases, the tests were implemented with the Statistics and Machine Learning Toolbox TM of Matlab (www.mathworks.com), and a significance level of 0.01 was used. As result, the number of those tests (in percentage) that rejected the null hypothesis where PSD fÃg indicates the mean PSD at a given repetition, turned out to be: 18.89 % for d, 27.41 % for h, 31.11 % for a, 54.44 % for b, and 71.11 % for c. Based on those results, our following analysis focused in the b and c bands, for which the change was such that PSD 5 \PSD 1 in most of the t tests (for more details, see Gutiérrez et al 2015). Two-way ANOVA tests were performed on the PSD of the selected brain rhythms.…”

Section: Resultsmentioning

confidence: 95%

“…Our results were in agreement with previous evidence of the involvement of b and c rhythms in the process of acquiring a new ability. In this paper we focused our attention in the decreasing trend of the b and c rhythms given that such trend was more obvious in those bands through our preliminary analysis (Gutiérrez et al 2015). However, a less evident increase in the a band could be observed in some subjects and in some sensors.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper (see also Gutiérrez et al 2015), we propose to study the brain activity measured through EEG during the process of acquiring a new ability. Hence, we analyze the power spectral densities (PSD) of various brain rhythms as a first step to find the link between learning and brain processes.…”

Section: Introductionmentioning

confidence: 99%

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

Gueorguiev

Ramírez-Moreno

2015

Cogn Neurodyn

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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.

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Section: Resultsmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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

Gueorguiev

Ramírez-Moreno

2015

Cogn Neurodyn

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“…This change in the former rhythm is related to motor learning activities, and the decrease in the latter band can be associated with temporal binding, which is the ability to group separate events that occur at different lapses of time [ 25 ]. Both datasets are freely available at [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Using Data Assimilation for Quantitative Electroencephalography Analysis

et al. 2020

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We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution of brain signals that are more likely (according to their coherence analysis) to be related to the cognitive process of interest. The power enhancement, due to the cognitive activity, is later validated in the power spectrum analysis by comparing through statistical tests relevant frequency content in two datasets in which assessing the development of cognitive abilities is of interest: the process of getting concentrated and of learning a new skill. Our results show that our DA-based methodology can highlight important frequency characteristics of the electroencephalogram (EEG) data that have been related to different cognitive processes. Hence, our proposal has the potential to understand of neurocognitive phenomena that is tracked through QEEG.

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“…This, in turn, raises confidence, persistency, and autonomy in the users toward the mastery of BCI skills. Furthermore, the identification of MI onset allows to analyze prior and post periods, which have been associated with the improvement of BCI performance and the recognition of the user cognitive state (Maeder et al, 2012 ; Bamdadian et al, 2014 ; Gutierrez et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Perception and Cognition of Cues Used in Synchronous Brain–Computer Interfaces Modify Electroencephalographic Patterns of Control Tasks

Alonso-Valerdi

Sepúlveda

Ramirez‐Mendoza

2015

Front. Hum. Neurosci.

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A motor imagery (MI)-based brain–computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain–computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest.

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Assessing the acquisition of a new skill with electroencephalography

Cited by 7 publications

References 9 publications

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

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

Using Data Assimilation for Quantitative Electroencephalography Analysis

Perception and Cognition of Cues Used in Synchronous Brain–Computer Interfaces Modify Electroencephalographic Patterns of Control Tasks

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