SHORT
COMMUNICATION
Movement automatization: motor interactions
and electroencephalogram application
Automatização do movimento: interações
motoras e aplicação do eletroencefalograma
João Marques Ferreira
Neto*, Diandra Caroline Martins*, Anderson de Sousa Escórcio*,
Gabriela Teles*, Monara Nunes*, Sávio Antoniel Almeida*, Maryanne
Torres Rodrigues**, Marco Antonio Orsini
Neves***, Janaina de Moraes Silva****, Silmar Silva
Teixeira*****, Eduardo Trajano******, Denise Flávio de Carvalho*******, Patricia Dusek********, Victor Hugo do Vale Bastos*
*Brain Mapping and Functionality Laboratory
(LAMCEF/UFPI), Federal University of Piauí, Brazil,
**Undergraduated in Physiotherapy from the College of
Sciences and Technology of Maranhão, Caxias, Brazil,
***Master Program in Applied Health Sciences, Vassouras
University, Master Program in Local Development UNISUAM/RJ, Brazil, Centro de Atenção e Saúde Funcional Ramon Pereira Freitas,
****Assistant professor, State University of Piauí
(UESPI), Health Sciences Center, Piauí, Brazil, *****Neuro-innovation Technology & Brain Mapping Laboratory,
Federal University of Piauí, Parnaíba,
Brazil, ******Master’s Program in Applied Health Sciences, Vassouras
University, Rio de Janeiro, Brazil, *******Centro de Atenção
e Saúde Funcional Ramon
Pereira Freitas, ********Master Program in Local
Development UNISUAM/RJ
Correspondence: Diandra Caroline Martins e Silva,
Federal University of
Piauí, Brazil. Email: diandra_martins@yahoo.com.br;
João Marques Ferreira Neto: marques0809@gmail.com; Anderson de Sousa Escórcio: anderson.escorcio99@gmail.com; Gabriela Teles:
gabitelesmonteiro@hotmail.com; Monara Nunes:
monarakedma@hotmail.com; Sávio Antoniel Almeida:
savioantoniel@outlook.com; Maryanne Torres Rodrigues:
maryanne-torres@hotmail.com; Marco Antonio Orsini Neves: orsinimarco@hotmail.com; Janaina de Moraes
Silva: fisiojanainams@gmail.com; Eduardo Trajano:
eduardolimatrajano@hotmail.com; Denise Flávio de Carvalho: denise.flavio40@globo.com; Patricia Dusek: patricia.dusek@unisuam.edu.br; Silmar Silva Texeira:
silmarteixeira@ufpi.edu.br; Victor Hugo do Vale Bastos: victorhugobastos@ufpi.edu.br
Abstract
The specificities of the motor system lead people to present skills to
perform some movements in an automatic way after learning. Acquiring the
automaticity of the movements is usually associated with reducing the demands
of attention control. Thus, automatization represents
a reduction in interference that undermines performance in dual task
conditions. It was carried out a search on the databases of Pubmed,
Scopus, SciELO e Lilacs, to understand the physiology
of automaticity and analyze the use of electroencephalogram as a means of
research in automatization. In this context, the study
aims to verify the employment of the electroencephalogram as a resource in the
analysis of the motor skills involved in the movement automatization.
Key-words: motion,
attention, electroencephalogram.
Resumo
As especificidades do
sistema motor levam às pessoas há apresentarem habilidades para realizar alguns
movimentos de maneira automática depois de aprendidos. Adquirir a
automaticidade dos movimentos geralmente está associadaà
redução das demandas de controle da
atenção. Assim, a automatização representa
uma redução da interferência que prejudica o
desempenho em condições de tarefa
dupla. Para este estudo foi realizado uma revisão integrativa de
estudos
indexados nas bases de dados Pubmed, Scopus, SciELO
e Lilacs, para compreender a fisiologia da automaticidade
e analisar a utilização do eletroencefalograma (EEG) como meio de investigação
na automatização. Neste contexto, o estudo tem por objetivo verificar o emprego
do eletroencefalograma como recurso na análise das habilidades motoras
envolvidas na automatização do movimento.
Palavras-chave: movimento, atenção,
eletroencefalograma.
An indisputable feature of the motor system is that people have the
ability to control some movements in an automatic way. In this way the
movements are performed without the attention being directed properly to the
details of the movement particularly for those requiring low precision or that
are commonly made [1]. Humans generally do not think about how to move the
lower extremities when they are walking, the viability
of the attention resources throughout the practice is caused by the automatization process. Theories of automaticity postulate
that the increase in the strength of a memory commonly benefits performance and
leads to automatization, whatever the representative
form of acquired memory [2,3].
In this way, automatization is a relevant
aspect of motor learning, because the motor skills are controlled with a high
degree of automatization as the experience increases.
In contrast, beginners tend to involve a greater amount of attention to control
their movements [4]. The motor automatization process
involves the function of specific cortical areas: it is considered that the
cerebellum, the motor area cingulate, the additional motor area and the putamen
have more significant connectivity, and that the
extent to which people they learn and become more skilled at performing
specific tasks, they typically use less global brain resources. Once the automatization of the improved movement captures less
working memory capacity [1,5,6].
One way to test movement automatization is by
performing dual tasks, in which the participant performs two activities
simultaneously. This type of method is an integral part of our daily operation.
During the day, we often need to perform motor tasks with all kinds of
cognitive abilities. For performance the dual task to be successful will depend
on the working memory capacity of an individual [6]. Typically, it is assumed
that during the dual task, each task consumes a portion of the capacity of the
working memory. If the combined processing demands of two tasks exceed the
capacity of the operating memory, interference will occur and performance on
one or both of these tasks is going to deteriorate. Thus, one way to improve dual
task performance is to reduce the demands placed on the working memory, then,
by increasing the automaticity of the movement [6].
While performing two tasks simultaneously, most of the processing
capacity is distributed to the main task. If the main task is relatively easy
or the individual presents skill in its realization, more attention capacity
can be directed to the secondary task, resulting in better performance in the
latter. In this way the performance in the secondary task can be considered as
an indirect reflection of the proficiency of the primary task [7]. A relevant
methodological problem of testing the automatization
of dual task tests derives from findings that are indicative of other
explanations for the reduction of dual task costs, in addition to the expected automatization of the motor control [4]. Some studies show
an effective integration of the two tasks (effective exchange of attention
between tasks) that have been repeatedly performed together, rather than an automatization of the main task to reduce the costs of dual
task [8].
Studies suggest analysis of automated motor skills with use of resources
such as electromyography (EMG) and magnetic resonance imaging, but these two
methods become difficult to access due to the high cost. In this way a way to
understand the cognitive processes involved in acquiring a new skill is the
electroencephalogram (EEG), which allows a record of electrical activities in
the cerebral cortex corresponding to the flow of information processed by the
cortex in activities during the execution of a motor task, be it complex,
simple, or during an exercise, with a temporal resolution greater than other
instruments [9]. The EEG is a method widely used in clinical and psychological
laboratories to monitor in a non-invasive way the brain activity, based on the
variations of voltages that are captured by different electrodes. The large
pyramidal neurons vertically oriented and located in cortical layers are the
main generators of the electrical fields of the EEG [10].
The incorporation of the motor gesture from the repetition of the motor
activity produces neural alterations capable of being detected with the use of
quantitative electroencephalogram (EEGq). The Alpha
frequency band has been correlated to cognitive processes, particularly fast
alpha (10 and 12 Hz), changes in the subject's
exposure to cognitive tasks of the most different levels of complexity. Beta is
considered a fast wave frequency (12 to 30 Hz) and appears to be the one that
is most related to motor activities, both premotors
and motor itself, having value for analyses related to normal and pathological
movements. The theta frequency band is associated with automatism and attention
processes, as well as being important for a variety of cognitive functions [11,12].
Although previous studies have already provided important aspects, the
internal mechanisms in which automatic control can be achieved is still open to
discussion, like in relevant studies that seek to verify the use of means of
neuronal research, such as EEG as a resource in the analysis of the motor
skills involved in the automatization of the
movement.