Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

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State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.

BackgroundRecent years have witnessed an increased interest in the use of steady state visual evoked potentials (SSVEPs) in brain computer interfaces (BCI), SSVEP is considered a stationary brain process that appears when gazing at a stimulation light source.New MethodsThe complex nature of brain processes advocates for non-linear EEG analysis techniques. In this work we explore the use of an Echo State Networks (ESN) based architecture for dynamical SSVEP detection.ResultsWhen simulating a 6-degrees of freedom BCI system, an information transfer rate of 49bits/min was achieved. Detection accuracy proved to be similar for observation windows ranging from 0.5 to 4 seconds.Comparison with existing methodsSSVEP detection performance has been compared to standard canonical correlation analysis (CCA). CCA achieved a maximum information transfer rate of 21 bits/minute. In this case detection accuracy increased along with the observation window lengthConclusionsAccording to here presented results ESN outperforms standard canonical correlation and has proved to require shorter observation time windows. However ESN and CCA approaches delivered diverse classification accuracies at subject level for various stimulation frequencies, proving to be complementary methods. A possible explanation of these results may be the occurrence of evoked responses of different nature, which are then detected by different approaches. While reservoir computing methods are able to detect complex dynamical patterns and/or complex synchronization among EEG channels, CCA exclusively captures stationary patterns. Therefore, the ESN-based approach may be used to extend the definition of steady-state response, considered so far a stationary process.HighlightsWe present a novel SSVEP dynamical detection approach based on ESN.This is the first time ESNs are applied to SSVEP based BCI systems.We provide experimental validation of proposed methodology.Experimental results indicate non-stationarity in SSVEP patterns.

Additional Title Page Footnotes:• We introduce a bursting tACS protocol to study semi-concurrent tACS effects in the visual system and their impact on behavior as measured by reaction time.• Burst 10 Hz tACS (tACS 10 ) applied to the visual cortex entrained γ-oscillations and increased RTs in a change-ofspeed detection visual task more than 70 Hz tACS (tACS 70 ) or Control conditions. • Burst tACS 10 also decreased amplitude of the P300 peak, while increasing α-power and γ-LZW complexity.• Physiological and behavioral impact of occipital tACS 10 and tACS 70 was frequency-specific. tACS 70 reduced γ-oscillations after 20min of tACS stimulation.• Cognitive task may determine cortical excitation levels as measured by complexity metrics, as lower γ-LZW complexity correlates to faster reaction times. SUMMARY:Little is known about the precise neural mechanisms by which tACS affects the human cortex. Current hypothesis suggest that transcranial current stimulation (tCS) can directly enhance ongoing brain oscillations and induce long-lasting effects through the activation of synaptic plasticity mechanisms [1]. Entrainment has been demonstrated in in-vitro studies, but its presence in non-invasive human studies is still under debate [2,3]. Here, we aim to investigate the immediate and shortterm effects of tACS bursts on the occipital cortex of participants engaged in a change-of-speed detection task, a task that has previously reported to have a clear physiology-behavior relationship, where trials with faster responses also have increased power in γ-oscillations (50-80 Hz) [4]. The dominant brain oscillations related to the visual task are modulated using multichannel tACS at 10 and 70 Hz within occipital cortex. We found that tACS stimulation at 10 Hz (tACS 10 ) enhanced both α (8-13 Hz) and γ oscillations, in hand with an increase in reaction time (RT) in the change-of-speed detection visual task. On the other hand, tACS at 70Hz desynchronized visual cortices, impairing both phase-locked and endogenous γ-power while increasing RT. While both tACS protocols seem to revert the relationship reported in [4], we argue that tACS produces a shift in attentional resources within visual cortex while leaving unaltered the resources required to conduct the task. This theory is supported by the fact that the correlation between fast RT and high γ-power trials is maintained for tACS sessions too. Finally, we measured cortical excitability by analyzing Event-Related-Potentials (ERP) Lempel-Ziv-Welch Complexity (LZW). In control sessions we observe that lower γ-LZW complexity correlates to faster reaction times. Both metrics are altered by tACS stimulation, as tACS 10 decreased amplitude of the P300 peak, while increasing γ-LZW complexity. To this end, our study highlights the nonlinear cross-frequency interaction between exogenous stimulation and endogenous brain dynamics, and proposes the use of complexity metrics, as LZW, to characterize excitability patterns of cortical areas in a behaviorally relevant timescale. These insights w...

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Objective:Attention-deficit hyperactivity disorder (ADHD) is the neurobehavioral disorder with the largest prevalence rate in childhood. ADHD is generally assessed based on physical examination of the child and interviews, and therefore prone to subjectivity. This fact may lead to a high risk of mis-and over-diagnosis, a problem that can be addressed through the use of objective markers. Methods:Here we propose to use phase-amplitude coupling as a digital biomarker in ADHD. We investigated the hypothesis that coupling between the phase of slow brain rhythms and the amplitude of fast rhythms is altered in the ADHD population. We tested this hypothesis measuring phase-amplitude coupling (PAC) in the 4 to 200Hz range in electroencephalographic (EEG) signals recorded in the central-frontal area in children during eyes closed resting state. Results:Using automatic clustering, we observed statistically significant beta-gamma PAC deficits in the ADHD population in the frontal-left hemisphere. Conclusions:These findings suggests alterations in the beta-gamma coupling in the ADHD population. We discuss the hypothesis that these alterations may be indicators of working memory and attention deficits. Significance:The study of the coupling between the different brain rhythms can potentially contribute to the understanding and clinical diagnosis of ADHD.