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Dubrovinskaya, N. V.; Machinskaya, R. I.

doi:10.1023/a:1020266516114

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The researches conducted within the real work have allowed to reveal the special pattern of EEG inherent in the vast majority of athletes and characterized by more expressed activity in a theta -and delta ranges, the insufficient relative power of bioelectric activity of a brain in the alpha range, the absolute power, bigger in comparison with "norm", in the alpha range in forward areas of a brain (this feature has been noted also by other researchers working with athletes (Popova et al, 2006)), the low size of the age index, higher size of the integrated index and easing of coherence. Comparison of the obtained data with data of references, namely: manifestation of more expressed delta activity and an alpha rhythm, smaller on power, -with aggression (Fishbein et al, 1989 ); bigger expressiveness of an alpha rhythm in frontal assignments -with tendency to risky activity (Kudrin et al, 2014); rhythms a theta range -with motivation (Dubrovinskaya and Machinskaya, 2002), including with motivation of achievement as property of the personality (Konareva, 2009), selective attention (Basar-Eroglu and Demiralp, 2001;Machinskaya et al, 2016) and procedural memory (Klimesch, 1997); the reduced coherence -with independent data processing; the low size of the age index, -not only with turning on of brake mechanisms, but also, most likely, being based on the data of Gevins et al (1997), Kamzanova (2011), and, in particular, Stroganova (2001), -with prevalence of endogenous attention, and higher size of the integrated index -with weakening of external influences due to creation of an internal cycle of work of neurons (Sokolov, 1969), -allows to speak about allocation of a peculiar " encephalographic portrait of the athlete" with "sports rage" inherent in him, ability to work in extreme conditions, abstraction if necessary from the environment, the result having high motivation on achievement and ability to expense of the maximum strong-willed efforts for achievement of the required purpose. Specification of this "portrait" and its change in process of self-control with biological feedback, certainly, are individual and in many respects are defined by set of different factors, including sexual distinctions.…”

Section: Discussionmentioning

confidence: 76%

Bioelectric activity of the brain and the predictive importance of effects of neurobiofeedback course at athletes

Черапкина¹

2018

Journal of Human Sport and Exercise - 2018 - Spring Conferences of Sports Science

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Works of the last years show activity of scientists on introduction of neurobiofeedback training in process of athletes training. At the same time, authors emphasize the need of further carrying out researches for this field of knowledge, first of all, caused by insufficiency of empirical data. The purpose of work was identification of athletes' brain features of bioelectric activity, and determination of the importance of neurobiofeedback effects in effectiveness forecasting of their competitive activity. With 101 athletes the neurobiofeedback course directed to increase in power of EEG in the alpha range by means of a hardware and software system of "BOSLAB-ALFA" was conducted. Prior to a training and after its termination by means of the 19-channel electroencephalograph "Neuron Range 3" "background" EEG registered. After a neurobiofeedback course the effectiveness of competitive activity at each athlete which was considered as successful was estimated if exceeded the result received at the same rank previous competitions. The statistical analysis of the obtained data was carried out by means of software packages of "StatPack" and "SPSS 13". Results of the conducted research have allowed to distinguish the peculiar " the athlete's encephalographic portrait" which is characterized by more expressed activity in a theta-and delta ranges, the insufficient relative power of brain bioelectric activity in the alpha range. And also to establish the informative indicators of post-training changes of EEG determining distinctions by success of competitive activity with an accuracy of recognition of 100%. Keeping EEG the signs characteristic of women, sportswomen differ from sportsmen in set of post

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Section: Discussionmentioning

confidence: 76%

Bioelectric activity of the brain and the predictive importance of effects of neurobiofeedback course at athletes

Черапкина¹

2018

Journal of Human Sport and Exercise - 2018 - Spring Conferences of Sports Science

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“…Figure 1 depicts the electrode location (left) and the Emotiv Epoc+ headset (right). According to the coherence analysis in attention [44,45], the selected electrodes were F3, F4, P7, and P8.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 7 shows an example of band power separation. For this paper, we use the electrodes F3, P7, F4, and P8 because they show high coherence in attention tasks [44,45].…”

Section: Band Power Separationmentioning

confidence: 99%

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Esqueda-Elizondo

Juárez‐Ramírez

López-Bonilla

et al. 2022

MCA

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Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

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A Review of Self-Report and Alternative Approaches in the Measurement of Student Motivation

2009

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Within psychological and educational research, self-report methodology dominates the study of student motivation. The present review argues that the scope of motivation research can be expanded by incorporating a wider range of methodologies and measurement tools. Several authors have suggested that current study of motivation is overly reliant on self-report measures, warranting a move toward alternative approaches. This review critiques self-report methodology as a basis for examining alternative conceptualizations of motivation (e.g., phenomenological, neuropsychological/physiological, and behavioral) and related measurement tools. Future directions in motivational methodology are addressed, including attempts at integration or combination of these approaches and a preliminary functional framework for the development of novel, multidimensional approaches to the study of motivation.Keywords Motivation . Measurement . Children . Review . Adolescents Motivation consists of the biological, physiological, social, and cognitive forces that direct behavior. Motivation has a long history within educational research (see reviews by Ball 1982;Weiner 1992;Young 1950). However, recent research has been driven by a predominant focus on the cognitive, intrapsychological aspects, discounting the importance of additional personal and contextual factors in the relationship between motivation and academic achievement. Past theories of motivation have focused on biological instincts, drives, and arousal. Current theories of achievement motivation, such as self-determination, cognitive evaluation, achievement goal, and expectancy-value theories, predominantly examine cognitive and, to a lesser extent, social processes that influence motivation for a particular activity.

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Untitled

Cited by 3 publications

References 13 publications

Bioelectric activity of the brain and the predictive importance of effects of neurobiofeedback course at athletes

Bioelectric activity of the brain and the predictive importance of effects of neurobiofeedback course at athletes

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

A Review of Self-Report and Alternative Approaches in the Measurement of Student Motivation

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