Electroencephalogram Analysis of Mechanisms Underlying Brain Activity during Voluntary Movement

Kuramoto, Nobuhisa; Ito, Shin‐ichi; Sato, Katsuya; Fujisawa, Shoichiro

doi:10.7763/ijbbb.2014.v4.309

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Alpha power was not subdivided into the rolandic mu (9–13 Hz) frequency, as it is typically recorded from central (C3, C4, Cz, Fz) electrode sites that are not included in the Emotiv neuroheadset used in the current study. Instead, we isolated the iAPF within the traditional 8–12 Hz bandwidth and focused statistical analyses on electrode sites overlying available and relevant sensorimotor areas including bilateral frontocentral (FC5, FC6) motor association cortex [ 29 ], superior temporal cortex (T7, T8) involved in perceptual learning [ 30 ] and sensory-guided KMI [ 31 ], inferior posterior temporal gyrus (P7, P8), and primary visual occipital cortex (O1, O2) [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Experience-dependent modulation of alpha and beta during action observation and motor imagery

et al. 2017

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BackgroundEEG studies investigating the neural networks that facilitate action observation (AO) and kinaesthetic motor imagery (KMI) have shown reduced, or desynchronized, power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands relative to rest, reflecting efficient activation of task-relevant areas. Functional modulation of these networks through expertise in dance has been established using fMRI, with greater activation among experts during AO. While there is evidence for experience-dependent plasticity of alpha power during AO of dance, the influence of familiarity on beta power during AO, and alpha and beta activity during KMI, remain unclear. The purpose of the present study was to measure the impact of familiarity on confidence ratings and EEG activity during (1) AO of a brief ballet sequence, (2) KMI of this same sequence, and (3) KMI of non-dance movements among ballet dancers, dancers from other genres, and non-dancers.ResultsBallet dancers highly familiar with the genre of the experimental stimulus demonstrated higher individual alpha peak frequency (iAPF), greater alpha desynchronization, and greater task-related beta power during AO, as well as faster iAPF during KMI of non-dance movements. While no between-group differences in alpha or beta power were observed during KMI of dance or non-dance movements, all participants showed significant desynchronization relative to baseline, and further desynchronization during dance KMI relative to non-dance KMI indicative of greater cognitive load.ConclusionsThese findings confirm and extend evidence for experience-dependent plasticity of alpha and beta activity during AO of dance and KMI. We also provide novel evidence for modulation of iAPF that is faster when tuned to the specific motor repertoire of the observer. By considering the multiple functional roles of these frequency bands during the same task (AO), we have disentangled the compounded contribution of familiarity and expertise to alpha desynchronization for mediating task engagement among familiar ballet dancers and reflecting task difficulty among unfamiliar non-dance subjects, respectively. That KMI of a complex dance sequence relative to everyday, non-dance movements recruits greater cognitive resources suggests it may be a more powerful tool in driving neural plasticity of action networks, especially among the elderly and those with movement disorders.

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Section: Methodsmentioning

confidence: 99%

Experience-dependent modulation of alpha and beta during action observation and motor imagery

et al. 2017

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“…On the other hand, [10] proposes motor imagery sequences based on EEG recordings of cortical (posterior parietal) motor cortex using local potential fields (LPFs), showing robustness in time in comparison to spike recordings, which has been also applied for assisted rehabilitation of leg movements, [11].…”

Section: Introductionmentioning

confidence: 99%

EEG-motor sequencing signals for online command of dynamic robots

Martinez-Peon

Parra-Vega

Sánchez‐Orta

2015

The 3rd International Winter Conference on Brain-Computer Interface

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The command of a dynamic entity with Brain-Computer Interfaces, based on a noninvasive low cost device that interprets EEG signals to infer commands, has been pursued with apparent success. However, little is understood on how to deal with the online dynamic response of the external robot. In this paper, sequences of neural activity during motor imagery are detected online to command a virtual dynamic robot in a Brain-Computer Interface. Two experiments were done. In the first one, the natural gradient approach is computed within an Independent Component Analysis to locate main sources of activity in single trials. In the second one, these sequences are used to make robust commands for a virtual dynamic robot. These results are achieved with few training. Clear evidence of the effectiveness of this scheme in reaching robotic tasks are obtained.

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Electroencephalogram Analysis of Mechanisms Underlying Brain Activity during Voluntary Movement

Cited by 2 publications

References 9 publications

Experience-dependent modulation of alpha and beta during action observation and motor imagery

Experience-dependent modulation of alpha and beta during action observation and motor imagery

EEG-motor sequencing signals for online command of dynamic robots

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