Using occipital ⍺-bursts to modulate behavior in real-time

Vigué‐Guix, Irene; Soto‐Faraco, Salvador

doi:10.1093/cercor/bhad217

Cited by 8 publications

(2 citation statements)

References 98 publications

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“…Finally, another future direction lies in the incorporation of novel neurophysiological markers for the mu frequency band in our framework. A growing number of studies have shown that the activity in this band can occur as longer-lasting bursts [119], or non-sinusoidal oscillations [120]. We believe that by adapting our approach to the characteristics of this frequency band, or by adopting alternative frameworks such as cycle-by-cycle analysis [121] we can uncover features that will further help us attain the goal of improving BCI robustness.…”

Section: Discussionmentioning

confidence: 99%

Beta bursts question the ruling power for brain-computer interfaces

Papadopoulos,

Szul,

Congedo

et al. 2023

Preprint

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Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery tasks. Current decoding approaches for such tasks take advantage of the fact that motor imagery generates time-locked changes in induced power in the sensorimotor cortex, and rely on band-pass filtered power changes or covariance matrices which also describe co-varying power changes in signals recorded from different channels. Although little is known about the dynamics of beta burst activity during motor imagery, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

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Section: Discussionmentioning

confidence: 99%

Beta bursts question the ruling power for brain-computer interfaces

Papadopoulos,

Szul,

Congedo

et al. 2023

Preprint

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“…The possibility that perception and cognition operate in cycles, following brain rhythms, has attracted researchers' interest for decades because it offers a unifying account of how brain oscillations contribute to prioritizing, categorising, and manipulating sensory input to provide accurate and fast responses to relevant stimuli. In addition, if brain rhythms and behaviour share an oscillatory "modus operandi", it may be possible to influence or control one (i.e., via entrainment) to change the other (Callaway & Yeager, 1960;Vigué-Guix et al, 2022;Vigué-Guix & Soto-Faraco, 2023;Zrenner et al, 2018). This would allow scientists to study how the brain processes information more directly and devise new interventions.…”

Section: Introductionmentioning

confidence: 99%

An #EEGManyLabs study to test the role of the alpha phase on visual perception (a replication and new evidence)

Ruzzoli,

Torralba Cuello,

Molinaro

et al. 2023

Preprint

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Several studies have suggested that low-frequency brain oscillations could be key to understanding how the brain samples sensory information via rhythmic alternation of low and high excitability periods. However, this hypothesis has recently been called into question following the publication of some null findings. As part of the #EEGManyLabs initiative, we set out to undertake a high-powered, multi-site replication of an influential study on this topic. In the original study, Mathewson et al. (2009) showed that during high amplitude fluctuations of alpha activity (8-13 Hz), the visibility of a visual target stimulus depended on the time the target was presented relative to the phase of the pre-target alpha activity. Furthermore, visual evoked potentials (e.g., N1, P1, P2 and P3) were larger in amplitude when the target was presented at the pre-stimulus alpha peaks, which were also associated with higher visibility. If we are successful in replicating the results of Mathewson et al. (2009), we intend to extend the original findings by conducting a second, original, experiment that varies the pre-stimulus time unpredictably to determine whether the phase-behavioural relationship depends on the target stimulus having a predictable onset time.

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