EEG and the Variance of Motor Evoked Potential Amplitude

Zarkowski, Paul; Shin, Cheolsu; Dang, Thong Q.; Russo, Joan; Avery, David

doi:10.1177/155005940603700316

Cited by 93 publications

(99 citation statements)

References 20 publications

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“…The increase of corticospinal output induced by tACS at ␤ range is consistent among subjects, and it seems to be potent enough to overcome the poor relationships between amplitude/ phase of oscillatory 20 Hz spontaneous activity of rolandic neurons and the size of motor responses evoked by TMS of the motor cortex (Zarkowski et al, 2006;Mitchell et al, 2007;Lepage et al, 2008;Sauseng et al, 2009). A likely explanation is that spontaneous oscillations and MEPs size reflect the excitability of the human motor system in overlapping, but not identical, neuronal populations (Mäki and Ilmoniemi, 2010).…”

Section: Discussionmentioning

confidence: 84%

Frequency-Dependent Tuning of the Human Motor System Induced by Transcranial Oscillatory Potentials

Feurra¹,

Bianco²,

et al. 2011

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Different corticothalamic brain modules intrinsically oscillate at a "natural frequency" in a topographically organized manner. In "quiescent" human sensorimotor regions, the main detectable oscillatory activity peaks at ϳ20 Hz, and partly contributes to determine the state of corticospinal excitability. Here, we showed that the transcranial application of an imperceptible, short-lasting (90 s) electric field oscillating at a physiological range increases corticospinal excitability online, with well defined frequency dependence and regional specificity. Indeed, the size of motor evoked potentials (MEPs) induced by navigated single-pulse TMS over the motor cortex significantly increased only during the local application of transcranial alternating current stimulation (tACS) at 20 Hz (␤ range). Other tACS frequencies (5, 10, and 40 Hz) applied on the motor cortex did not impact MEPs' size. Moreover, tACS applied on a control site (parietal cortex) and on a peripheral site (ulnar nerve) also failed to modulate MEPs. These results help clarifying the functional significance of the 20 Hz idling ␤ rhythm of sensorimotor regions and suggest potential clinical applications of this approach.

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Section: Discussionmentioning

confidence: 84%

Frequency-Dependent Tuning of the Human Motor System Induced by Transcranial Oscillatory Potentials

Feurra¹,

Bianco²,

et al. 2011

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“…At first glance this might seem somewhat surprising as previous studies found such associations. Zarkowski and colleagues [10], for example, found a strong relation between prestimulus alpha and gamma amplitudes and elicited MEP amplitude size. Similarly, Sauseng et al [9] report the strength of prestimulus alpha band amplitude predicting whether a TMS pulse would elicit an MEP or not, and Mäki and Ilmoniemi [14] found a strong correlation between MEP size and EEG beta amplitude.…”

Section: Discussionmentioning

confidence: 99%

“…The authors conclude that the magnitude of corticospinal excitability is determined by the amount of topographically specific alpha oscillations in the sensorimotor cortex. Additionally, Zarkowski and colleagues [10] were able to show that not only alpha band activity is a determinant of whether TMS elicits an MEP or not but also other frequency bands such as gamma. They found a highly significant relation between prestimulus gamma band activity and elicited MEP amplitude size.…”

Section: Introductionmentioning

confidence: 99%

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EEG Oscillatory Phase-Dependent Markers of Corticospinal Excitability in the Resting Brain

Berger

Minarik

Liuzzi

et al. 2014

BioMed Research International

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Functional meaning of oscillatory brain activity in various frequency bands in the human electroencephalogram (EEG) is increasingly researched. While most research focuses on event-related changes of brain activity in response to external events there is also increasing interest in internal brain states influencing information processing. Several studies suggest amplitude changes of EEG oscillatory activity selectively influencing cortical excitability, and more recently it was shown that phase of EEG activity (instantaneous phase) conveys additional meaning. Here we review this field with many conflicting findings and further investigate whether corticospinal excitability in the resting brain is dependent on a specific spontaneously occurring brain state reflected by amplitude and instantaneous phase of EEG oscillations. We applied single pulse transcranial magnetic stimulation (TMS) over the left sensorimotor cortex, while simultaneously recording ongoing oscillatory activity with EEG. Results indicate that brain oscillations reflect rapid, spontaneous fluctuations of cortical excitability. Instantaneous phase but not amplitude of oscillations at various frequency bands at stimulation site at the time of TMS-pulse is indicative for brain states associated with different levels of excitability (defined by size of the elicited motor evoked potential). These results are further evidence that ongoing brain oscillations directly influence neural excitability which puts further emphasis on their role in orchestrating neuronal firing in the brain.

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“…The muscular movements effect the EEG oscillations above 45 Hz. The high gamma band is shown to have the highest correlation with motor-evoked potentials (MEPs) [25]. A first-order band-pass Butterworth filter with a pass-band of 5-45 Hz is used to remove artefacts caused by biological movements.…”

Section: Preprocessingmentioning

confidence: 99%

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

et al. 2017

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Abstract:The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

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EEG and the Variance of Motor Evoked Potential Amplitude

Cited by 93 publications

References 20 publications

Frequency-Dependent Tuning of the Human Motor System Induced by Transcranial Oscillatory Potentials

Frequency-Dependent Tuning of the Human Motor System Induced by Transcranial Oscillatory Potentials

EEG Oscillatory Phase-Dependent Markers of Corticospinal Excitability in the Resting Brain

A Game Player Expertise Level Classification System Using Electroencephalography (EEG)

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