Electrical brain oscillations reflect fluctuations in neural excitability. Fluctuations in the alpha band (α,(8)(9)(10)(11)(12) 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 offline approaches to confirm α-gated cyclic patterns.However, a particularly relevant implication is the possibility to use this principle online for realtime 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.

ABSTRACTElectrical 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 offline approaches to confirm α-gated cyclic patterns. However, a particularly relevant implication is the possibility to use this principle online 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.

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.

Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8–13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.

Shifts in spatial attention are associated with variations in alpha-band (α, 8–14 Hz) activity, specifically in inter-hemispheric imbalance. The underlying mechanism is attributed to local α-synchronisation, which regulates local inhibition of neural excitability, and fronto-parietal synchronisation reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCI). In the present study, we explored whether long-range α-synchronisation presents lateralised patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data shows a lateralised pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronisation with support vector machines (SVM) was at chance-level. The present findings suggest EEG may not be capable of detecting long-range α-synchronisation in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.SIGNIFICANCE STATEMENTCognitive neuroscience advances should ideally have a real-world impact, with an obvious avenue for transference being BCI applications. The hope is to faithfully translate user-generated brain endogenous states into control signals to actuate devices. A paramount challenge for transfer is to move from group-level, multi-trial average approaches to single-trial level. Here, we evaluated the feasibility of single-trial estimation of phase synchrony across distant brain regions. Although many studies link attention to long-range synchrony modulation, this metric has never been used to control BCI. We present a first attempt of a synchrony-based BCI that, albeit unsuccessful, should help break new ground to map endogenous attention shifts to real-time control of brain-computer actuated systems.