Can the occipital alpha-phase speed up visual detection through a real-time EEG-based brain-computer interface (BCI)?

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>Electrical brain oscillations reflect fluctuations in neural excitability. Fluctuations in the alpha band (α, 8-12 Hz) in the occipito-parietal cortex are thought to regulate sensory responses, leading to cyclic variations in visual perception. Inspired by this theory, some past and recent studies have addressed the relationship between α-phase from extra-cranial EEG and behavioural responses to visual stimuli in humans. The latest studies have used <jats:italic>offline</jats:italic> approaches to confirm α-gated cyclic patterns. However, a particularly relevant implication is the possibility to use this principle <jats:italic>online</jats:italic> for real-time neurotechnology, whereby stimuli are time-locked to specific α-phases leading to predictable outcomes in performance. Here we aimed at providing a proof-of-concept for such real-time neurotechnology. Participants performed a speeded response task to visual targets that were presented upon a real-time estimation of the α-phase via an EEG closed-loop brain-computer interface (BCI). We predicted, according to the theory, a modulation of reaction times (RTs) along the α-cycle. Our BCI system achieved reliable trial-to-trial phase-locking of stimuli to the phase of individual occipito-parietal α-oscillations. Yet, the behavioural results did not support a consistent relation between RTs and the phase of the α-cycle neither at group nor single participant levels. We must conclude that although the α-phase might play a role in perceptual decisions from a theoretical perspective, its impact on EEG-based BCI application appears negligible.</jats:p>

Rhythms in cognition: The evidence revisited

“…A different path forward may be using closed‐loop brain‐computer interfaces (BCIs) to draw stronger associations between brain rhythms and behaviour. Although this approach requires a high level of technical sophistication, it has been used in the past (Callaway & Yeager, 1960) and more recently to study rhythmic sampling (Ramot & Martin, 2022; Zrenner et al, 2016; Vigué‐Guix et al, 2020, this issue). One advantage is that closed‐loop BCIs reduce the degrees of freedom in the signal processing pipeline because the analysis must be conducted in real‐time; therefore, the features of interest and the parameters to extract them have to be set a priori.…”

“…For instance, the exact sampling frequencies implicated in cognitive functions often differ between studies and have been shown to depend on the task and/or stimulus characteristics (Chen et al, 2020; Ho et al, 2017; Merholz et al, 2022; Ronconi et al, 2017; also see table in Ruzzoli et al, 2019), which is difficult to reconcile with any simple model of fixed, discrete temporal ‘frames’ (White, 2018). Additionally, the effect sizes in studies showing periodicity in behavioural or neural measures tend to be small, hence not in line with clear, all‐or‐nothing frame boundaries (Milton & Pleydell‐Pearce, 2016; White, 2018) or utility for real‐life applications (Vigué‐Guix et al, 2020). Furthermore, the literature suffers from a lack of direct replications, pre‐registered studies, data, and code sharing (Garrett‐Ruffin et al, 2021; Niso et al, 2021; Pavlov et al, 2021), as well as low statistical power (Button et al, 2013).…”

Forgotten rhythms? Revisiting the first evidence for rhythms in cognition

“…Science is a cumulative endeavour, and it should go without saying that building on what has gone before can help to prevent redundant research or to inspire replication attempts (e.g., Vigué‐Guix et al, 2020 in this special issue). Although we are all responsible for reading past work in our chosen research fields, how far back you choose to look is obviously your own decision.…”

Low pre‐stimulus EEG alpha power amplifies visual awareness but not visual sensitivity

“…Additionally, we investigated the relationship between pre‐stimulus oscillatory phase and perception, for which current evidence is mixed. Whilst many studies have linked the phase of oscillatory activity in specific frequency bands (before or during stimulus onset) to the likelihood of perception (Busch et al., 2009; Busch & VanRullen, 2010; Mathewson et al., 2009; Samaha et al., 2015), others have been unable to replicate these findings (Benwell et al., 2017; van Diepen et al., 2015; Ruzzoli et al., 2019; Vigué‐Guix et al., 2020; see also Brüers & VanRullen, 2017). Together, our analyses aim to contribute to the understanding of the mechanisms by which baseline neural activity impacts visual perception.…”