Towards real-time visualization of a juggler’s brain

Schiavone, Giuseppina; Großekathöfer, Ulf; Campo, Simon à; Mihajlović, Vojkan

doi:10.1080/2326263x.2015.1101656

Cited by 6 publications

(10 citation statements)

References 28 publications

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“…We identified unique patterns of inter-brain connectivity as well as several differences between the intra-brain functional networks of the two jugglers as task demands increased, suggesting that skill-level (e.g., expert vs. novice) may influence hyperbrain dynamics. Previous evidence supports differences in functional brain connectivity between expert and non-expert jugglers during individual juggling tasks (Schiavone et al, 2015), and some hyperscanning studies have examined hyperbrain connectivity between two experts (e.g., Sänger et al, 2012; Toppi et al, 2016). However, to date the effects of skill-level on hyperbrain dynamics during interpersonal tasks have not been systematically explored.…”

Section: Introductionmentioning

confidence: 78%

“…In the current study, we limited our functional connectivity analysis to the alpha frequency band because it has been shown to be an important marker of human social coordinated behavior (Tognoli et al, 2007) and because our previous research identified differences in hyperbrain functional connectivity between expert and non-expert jugglers at alpha band frequencies (Filho et al, 2017). Nevertheless, additional differences in functional connectivity are likely to occur at other frequency bands (Schiavone et al, 2015). Examining functional connectivity in other bands and directly comparing functional networks across bands could yield further insights into differences between individual and interactive juggling.…”

Section: Discussionmentioning

confidence: 99%

“…Advances in neuroimaging, which permit the acquisition of brain data in ecological settings during active movement (Thompson et al, 2008), have allowed opportunities to study these processes at the neurophysiological level. As a consequence, juggling has been used to investigate skill acquisition, motor learning, and expert performance by examining movement related cortical potentials and functional and structural brain connectivity (Gerber et al, 2014; Schiavone et al, 2015; Berchicci et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity

Stone

Tamburro

Filho

et al. 2019

Front. Hum. Neurosci.

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Hyperscanning studies, wherein brain activity is recorded from multiple participants simultaneously, offer an opportunity to investigate interpersonal dynamics during interactive tasks at the neurophysiological level. In this study, we employed a dyadic juggling paradigm and electroencephalography (EEG) hyperscanning to evaluate functional connectivity between EEG sources within and between jugglers’ brains during individual and interactive juggling. We applied graph theoretical measures to identify significant differences in functional connectivity between the individual and interactive juggling conditions. Connectivity was measured in multiple juggler pairs with various skill levels where dyads were either skill-level matched or skill-level unmatched. We observed that global efficiency was reduced during paired juggling for less skilled jugglers and increased for more skilled jugglers. When jugglers were skill-level matched, additional reductions were found in the mean clustering coefficient and small-world topology during interactive juggling. A significant difference in hemispheric brain lateralization was detected between skill-level matched and skill-level unmatched jugglers during interactive juggling: matched jugglers had an increased right hemisphere lateralization while unmatched jugglers had an increased left hemisphere lateralization. These results reveal multiple differences in functional brain networks during individual and interactive juggling and suggest that similarities and disparities in individual skills can impact inter-brain dynamics in the performance and learning of motor tasks.

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Section: Introductionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity

Stone

Tamburro

Filho

et al. 2019

Front. Hum. Neurosci.

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“…As the individual becomes more skilled, mental load decreases, even though precision increases. This effect can be observed when comparing EEG signals of beginners and experts Schiavone et al (2015). It would seem the practitioner moves from more predictive based control to reaction.…”

Section: Introductionmentioning

confidence: 94%

Model-Free All-Source-All-Destination Learning as a Model for Biological Reactive Control

Knudsen¹,

Hendseth²,

Tufte³

et al. 2021

MIC

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We present here a model-free method for learning actions that lead to an all-source-all-destination shortest path solution. We motivate our approach in the context of biological learning for reactive control. Our method involves an agent exploring an unknown world with the objective of learning how to get from any starting state to any goal state in shortest time without having to run a path planning algorithm for each new goal selection. Using concepts of Lyapunov functions and Bellman's principle of optimality, our agent learns universal state-goal distances and best actions that solve this problem.

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“…Presentations described providing ALS patients with painting tools for home use [ 115 ], an initiative to organize a design competition, and a neuro-catwalk fashion show displaying designs of attractive and artistically satisfying BCI headsets. Research was presented on what goes on in the brain of a juggler and whether that information can be visualized or sonified to make a performance even more attractive [ 116 ]. What goes on in the brains of readers of fiction?…”

Section: Bcis For Specific Populations/applicationsmentioning

confidence: 99%

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Huggins

Guger

Ziat

et al. 2017

Brain-Computer Interfaces

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The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

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Towards real-time visualization of a juggler’s brain

Cited by 6 publications

References 28 publications

Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity

Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity

Model-Free All-Source-All-Destination Learning as a Model for Biological Reactive Control

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

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