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

Abstract

Spontaneous oscillatory neural activity can influence the processing of incoming sensory input and subsequent behavioural reactions. Spontaneous oscillatory activity mostly appears in stochastic bursts, but typical trial-averaged approaches fail to capture this. We aimed at relating oscillatory bursts in the alpha band (8-13 Hz) to behaviour directly, via an EEG-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time in a visual detection task. According to alpha theories, we hypothesised that targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts should lead to faster responses and higher false alarm rates. Our findings support the role of bursts in alpha-oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioural theories.

Double dissociation of spontaneous alpha-band activity and pupil-linked arousal on additive and multiplicative perceptual gain

“…The additive gain we observed suggests that states of low alpha should not lead to any change in perceptual sensitivity (d’) since low alpha boosts detection reports even in the absence of any stimulus (i.e., false alarms). This is indeed what our SDT analyses revealed and is congruent with multiple recent studies showing an effect of alpha on detection criterion, but not sensitivity (18–21, 49). Our results build on this prior literature by showing that the effect of alpha on detection criterion is not restricted to just the case of threshold stimuli but is also observed across the range of contrast levels spanning each individuals CRF, and also by showing that the criterion effect holds after controlling for the correlation between ongoing alpha and pupil size.…”

Beta bursts question the ruling power for brain-computer interfaces

“…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.…”

Surfing beta burst waveforms to improve motor imagery-based BCI

“…Standard techniques for analyzing meso-and macro-scale neural signals recorded during the execution or imagination of movements typically rely on signal power metrics assuming that relevant changes in brain signals are reflected in amplitude modulation (Alayrangues et al, 2019;Kilavik et al, 2013;Pfurtscheller, 1981;Pfurtscheller et al, , 1996Pfurtscheller and Lopes da Silva, 1999). However, there has recently been a considerable paradigm shift towards considering transient signal features on the single trial level (Chen et al, 2021;Coleman et al, 2024;Jones, 2016;Little et al, 2019;Lundqvist et al, 2024Lundqvist et al, , 2016Rayson et al, 2023;Shin et al, 2017;Torrecillos et al, 2018;Vigué-Guix and Soto-Faraco, 2022;Wessel, 2020). Therefore, considering that computational models describing the neuronal generators of specific burst waveform shapes (Bonaiuto et al, 2021;Sherman et al, 2016;Szul et al, 2023) offer an improved theoretical interpretability of the observed signal modulations, applications leveraging such signal characteristics, like beta bursts, could potentially benefit from incorporating recent neuroscience findings (Papadopoulos et al, 2022).…”