Boosting Generalization in Bio-Signal Classification by Learning the Phase-Amplitude Coupling

Various hand-crafted features representations of bio-signals rely primarily on the amplitude or power of the signal in specific frequency bands. The phase component is often discarded as it is more sample specific, and thus more sensitive to noise, than the amplitude. However, in general, the phase component also carries information relevant to the underlying biological processes. In fact, in this paper we show the benefits of learning the coupling of both phase and amplitude components of a bio-signal. We do so by introducing a novel self-supervised learning task, which we call Phase-Swap, that detects if bio-signals have been obtained by merging the amplitude and phase from different sources. We show in our evaluation that neural networks trained on this task generalize better across subjects and recording sessions than their fully supervised counterpart.Comment: Accepted at GCPR 202

Effects of unilateral dynamic handgrip on reaction time and error rate

Quick and accurate reactions to environmental stimuli are often required. Researchers have investigated ways to improve these reactions, which are critical components of perceptual-motor abilities. To optimize individual performance, different techniques, such as embodied interventions and brain stimulation, have been examined. The evidence from EEG studies shows that upper limb muscle contractions lead to changes in brain oscillations associated with changes in mental states and behavioral outcomes. Much research has been conducted on whether muscle contractions of a particular hand have a greater effect on a perceptual-motor ability, as a trigger to facilitate cortical processes (a mediator) for skilled motor performance. While previous studies have shown that left- (vs. right-) hand contractions can lead to greater alpha activation, we hypothesized that left dynamic handgrips have different impacts on motor performance, reflected by simple RT (SRT) and choice RT (CRT). We recruited 64 right-handers, for a within/between-subjects experiment consisting of performance measurements in SRT and CRT tasks after the intervention (either right or left dynamic handgrip approximately twice a second for 30 s for each hand) or assignment to paired passive control groups. We did not find left-hand contractions improve response accuracy in neither SRT nor CRT tasks. Further, left-hand contractions did not affect RTs. The findings indicate that the effects of dynamic handgrips are smaller on behavioral outcomes such as RTs than what can be inferred from published studies. More research is needed to establish the effect of dynamic handgrips on optimizing performance. © 2022, The Author(s)

Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy

Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals)

Duration of Coherence Intervals in Electrical Brain Activity in Perceptual Organization

We investigated the relationship between visual experience and temporal intervals of synchronized brain activity. Using high-density scalp electroencephalography, we examined how synchronized activity depends on visual stimulus information and on individual observer sensitivity. In a perceptual grouping task, we varied the ambiguity of visual stimuli and estimated observer sensitivity to this variation. We found that durations of synchronized activity in the beta frequency band were associated with both stimulus ambiguity and sensitivity: the lower the stimulus ambiguity and the higher individual observer sensitivity the longer were the episodes of synchronized activity. Durations of synchronized activity intervals followed an extreme value distribution, indicating that they were limited by the slowest mechanism among the multiple neural mechanisms engaged in the perceptual task. Because the degree of stimulus ambiguity is (inversely) related to the amount of stimulus information, the durations of synchronous episodes reflect the amount of stimulus information processed in the task. We therefore interpreted our results as evidence that the alternating episodes of desynchronized and synchronized electrical brain activity reflect, respectively, the processing of information within local regions and the transfer of information across regions

Mindfulness Meditation versus EEG-Alpha Neurofeedback: The Role of EEG-Alpha Enhancement in Attentional Control

This thesis directly compared two active interventions known to enhance the EEG-Alpha rhythm, mindfulness meditation (MM) with EEG-Alpha enhancement neurofeedback (NFB), relative to a non-active Sham-NFB control. Seventy-three university students were randomized to one of the three 15-minute single-session interventions. Participants were subsequently compared on their ability to enhance EEG-Alpha amplitude as well as regarding Stroop behavioural performance, EEG event-related potentials, and EEG-Alpha event-related desynchronization (ERD) as markers of attentional control. Participants randomized to MM, NFB, and Sham did not differ in their ability to modulate the EEG-Alpha rhythm post-intervention. However, enhancements in EEG-Alpha amplitude were seen within the MM and Alpha-NFB groups during these interventions. Participants randomized to MM and NFB exhibited reduced ERD during performance of the Stroop task, interpreted as reflecting reduced cognitive effort required for task performance. However, these were not accompanied by any group differences in Stroop behavioural performance or P300 amplitudes. This study provides preliminary support for the therapeutic potential of single-session treatments that target the EEG-Alpha rhythm, such as MM and NFB, to influence neural processes underlying attentional control. Further evaluation of the benefits of these interventions across multiple sessions is indicated

LONG-LASTING EFFECTS OF MTBI ON OCULOMOTOR ABILITY AND NEUROMUSCULAR CONTROL

Concussions result in short-lived to long-lasting neurological function impairment and disturbances, typically undetectable by standard neuroimaging protocols, which can persist for several months post-trauma. Eye-tracking and virtual reality can be a powerful tool in the assessment of short- and long-term concussed individuals. However, it needs a clear and concise methodology. When acting as an optical flow-induced perturbation of balance metrics and combined with electroencephalographic data, it can differentiate between a non-concussed fatigue state and a concussive state. Furthermore, when employed as a secondary cognitive task, it elicits neural modulations and postural control perturbations that can detect concussion-related impairments up to eight years post-trauma. In this dissertation we sought to (i) develop a virtual reality environment that implements known eye-tracking methodologies and validate its accuracy in differentiating between non-concussed and concussed cohorts, (ii) investigate the presence of neural signatures that could differentiate between a concussive state and a fatigue state, and (iii) determine if long-lasting oculomotor and peripheral muscle control impairments could be reliably detected in a concussed cohort several years post-trauma. Our overarching hypotheses were that (i) eye-tracking metrics observed in a virtual reality environment can differentiate between non- concussed and concussed cohorts, (ii) spectral power of cortical activations are different between non-concussed participants in a fatigued state and concussed participants, and (iii) oculomotor impairments and corticomuscular correlates of balance metrics can be detected in a concussed several months post-trauma. Our findings support the majority of the initial proposed investigation. We detected corticomuscular coherence and postural control differences capable of differentiating between non-concussed and long-term concussed participants, established a link between corticomuscular coherence and postural control adaptations observed in the concussed group, determined some limitations of virtual reality paradigms in concussion assessment

LONG-LASTING EFFECTS OF MTBI ON OCULOMOTOR ABILITY AND NEUROMUSCULAR CONTROL

Concussions result in short-lived to long-lasting neurological function impairment and disturbances, typically undetectable by standard neuroimaging protocols, which can persist for several months post-trauma. Eye-tracking and virtual reality can be a powerful tool in the assessment of short- and long-term concussed individuals. However, it needs a clear and concise methodology. When acting as an optical flow-induced perturbation of balance metrics and combined with electroencephalographic data, it can differentiate between a non-concussed fatigue state and a concussive state. Furthermore, when employed as a secondary cognitive task, it elicits neural modulations and postural control perturbations that can detect concussion-related impairments up to eight years post-trauma. In this dissertation we sought to (i) develop a virtual reality environment that implements known eye-tracking methodologies and validate its accuracy in differentiating between non-concussed and concussed cohorts, (ii) investigate the presence of neural signatures that could differentiate between a concussive state and a fatigue state, and (iii) determine if long-lasting oculomotor and peripheral muscle control impairments could be reliably detected in a concussed cohort several years post-trauma. Our overarching hypotheses were that (i) eye-tracking metrics observed in a virtual reality environment can differentiate between non- concussed and concussed cohorts, (ii) spectral power of cortical activations are different between non-concussed participants in a fatigued state and concussed participants, and (iii) oculomotor impairments and corticomuscular correlates of balance metrics can be detected in a concussed several months post-trauma. Our findings support the majority of the initial proposed investigation. We detected corticomuscular coherence and postural control differences capable of differentiating between non-concussed and long-term concussed participants, established a link between corticomuscular coherence and postural control adaptations observed in the concussed group, determined some limitations of virtual reality paradigms in concussion assessment

Komplexe dynamische Systeme als funktionelle Netzwerke : Möglichkeiten und Grenzen der datengetriebenen Analyse

Aus einer Vielzahl von Subsystemen zusammengesetzte dynamische Systeme können als Netzwerk aufgefasst und mit Methoden der Graphentheorie beschrieben werden. Innerhalb dieses Ansatzes werden die Subsysteme durch Knoten und die Interaktionen zwischen den Subsystemen durch Kanten repräsentiert. Ein Beispiel für ein solches System ist das menschliche Gehirn, in dem die Interaktionen zwischen mehreren Hirnregionen für die Gesamtfunktion des Gehirns essentiell sind. Solche Netzwerke werden als funktionelle Hirnnetzwerke bezeichnet. Die Abbildung des zu untersuchenden Systems auf ein Netzwerk ist allerdings nicht eindeutig. Insbesondere müssen Knoten und Kanten des Netzwerks bestimmt werden. Von jedem Subsystem muss zunächst eine geeignete Observable gemessen werden. Aus den Zeitreihen der Observablen werden mit einem geeigneten Verfahren jeweils paarweise die Interaktionstärken bzw. -richtungen geschätzt und daraus mithilfe einer Transferfunktion die Kanten des resultierenden Netzwerks bestimmt. Im Rahmen dieser Arbeit wird am Beispiel des menschlichen Gehirns erstmals umfassend untersucht, inwieweit sich dieser Konstruktionsprozess auf die Struktur der resultierenden funktionellen Netzwerke auswirkt. Dazu werden funktionelle Netzwerke aus Aufzeichnungen neuronaler Aktivität mittels Elektro- und Magnetoenzephalographie während verschiedener, aber bekannter, physiologischer und pathophysiologischer Zustände auf unterschiedliche Weisen konstruiert und die Struktur der resultierenden Netzwerke mittels netzwerk- und knotenspezifischer Kenngrößen miteinander verglichen. Dabei zeigte sich, dass insbesondere die Methode zur Messung neuronaler Aktivität und die Transferfunktion, welche die gemessenen Interaktionsstärken auf die Kanten abbildet, einen großen Einfluss haben. Ob ein linearer oder nicht-linearer Ansatz zur Schätzung der Interaktionsstärke gewählt wurde war nicht entscheidend. Darüber hinaus wurde gezeigt, dass sich auch komplexere Netzwerkphänomene wie der epileptische Prozess oder kognitive Prozesse in der Struktur funktioneller Hirnnetzwerke widerspiegeln und sich mithilfe des Netzwerkansatzes sinnvoll charakterisieren lassen. Die Analyse der Langzeitvariabilität funktioneller Netzwerke epileptischer Gehirne ergab, dass netzwerkspezifische Kenngrößen neben dem epileptischen Prozess auch eine Reihe physiologischer Prozesse, vor allem Tag-Nacht-Rhythmen, widerspiegeln. Für knotenspezifische Kenngrößen wurde hingegen ein Einfluss des räumlichen Abtastens der Hirnregionen beobachtet. Die Beschreibung komplexer dynamischer Systeme mit einem Netzwerkansatz unter Berücksichtigung der in dieser Arbeit gefundenen Einflussfaktoren verspricht eine bessere Charakterisierung und ein tieferes Verständnis dieser Systeme.Complex Dynamical Systems as Functional Networks: Opportunities and Limitations of Data-Driven Analysis Dynamical systems composed of many distinct subsystems can be regarded as a network and be characterized by graph theoretical methods. Within this approach subsystems are represented as nodes and interactions among them as links oder edges. One example for such a system is the human brain, in which interactions between several brain regions are fundamental for the overall functioning. Such networks are referred to as functional brain networks. The mapping of the system under study onto a network is, however, equivocal. In particular nodes and edges have to be determined. First, for each subsystem an appropriate observable has to be chosen. Second, from the time series of these observables pairwise interaction strengths and directions are estimated with a suitable time series analysis technique. Finally, the estimated strengths and directions of interaction are mapped with a suitable transfer function onto the edges of the resulting network. In this thesis the extent of the impact of this construction process on the structure of the resulting functional networks was for the first time comprehensively studied using the example of the human brain. For this purpose functional brain networks were constructed from recordings of neuronal activity with electro- and magnetoencephalography during different, but known, physiological and pathophysiological states in different ways and the structure of the resulting networks was compared using network and node specific characteristics. It was observed that, particularly the method for measuring neuronal activity and the transfer function, mapping the interaction strength on the edges, have a large impact, while choosing a linear or a non-linear approach for estimating the interaction strength was not crucial. Furthermore it could be shown that also more complex network phenomena such as the the epileptic process or cognitive processes are reflected by the structure of functional brain networks and can be characterized meaningfully within the network approach. The analysis of the longterm variability of functional epileptic brain networks revealed that network specific characteristics reflect - beside the epileptic process - also a variety of physiological processes, particularly daily rhythms. Node specific characteristics, however, have been observed to be influenced by the spatial sampling. Taking into account the influencing factors identified in this study the description of complex dynamical systems within the network approach promises a better characterization and a deeper understanding of these systems