Multimodal attention in a simulated driving environment - Novel approaches to the quantification of attention based on brain activity

The concept of attention is an established focus of study in neurosciences. The quantification of attention during driving can help identify situations in which the driver is not completely aware of the situation. This work deals with the implementation of a setup to simulate a driving environment that enables audiovisual tasks to be embedded into the driving task while acquiring biosignals such as electroencephalography. The main goal of this dissertation was to find a correlation between attention and brain activity as seen on the electroencephalographic activity while driving. By using the principle of phase-amplitude coupling in electroencephalographic signals, it was hypothesized that Theta-Gamma phase-amplitude coupling might correlate to multimodal attention and thus might be eligible as a biomarker of attention in tasks such as driving. Surface electroencephalography was measured simultaneously in drivers and copilots while participating in simulated driving scenarios with varying multimodal attentional demands. The phase-amplitude coupling between Theta-band phase and Gamma-band amplitude from the electroencephalograpic signal was obtained and evaluated. Results showed significant phase-amplitude coupling differences between drivers and copilots in areas related to multimodal attention (prefrontal cortex, frontal eye fields, primary motor cortex, and visual cortex). The results were confirmed by behavioral data acquired during the test (detection task). We conclude that phase-amplitude coupling does function as a biomarker for attentional demand by detecting cortical areas being activated through specific multimodal (in this case, driving) tasks. Additionally, the data acquired in the main work of this thesis was used to test an auditory stimulus reconstruction algorithm previously tested by our work group. The stimulus reconstruction allowed to obtain post-hoc additional information regarding attentional effort during driving (success of the stimulus reconstruction was significantly correlated to auditory effort) and serves as a compliment to the main results. This dissertation thus offers an insight on attentional systems in multimodal situations and the neurophysiological systems underlying attention. It develops methods to measure attention in a driving environment, both as seen using phase-amplitude coupling and by being able to single out auditory effort by reconstructing the auditory stimuli. Finally, these methods can be translated to other activities since they are both based on non-invasive electroencephalography

From locomotion to dance and back : exploring rhythmic sensorimotor synchronization

Le rythme est un aspect important du mouvement et de la perception de l’environnement. Lorsque l’on danse, la pulsation musicale induit une activité neurale oscillatoire qui permet au système nerveux d’anticiper les évènements musicaux à venir. Le système moteur peut alors s’y synchroniser. Cette thèse développe de nouvelles techniques d’investigation des rythmes neuraux non strictement périodiques, tels que ceux qui régulent le tempo naturellement variable de la marche ou la perception rythmes musicaux. Elle étudie des réponses neurales reflétant la discordance entre ce que le système nerveux anticipe et ce qu’il perçoit, et qui sont nécessaire pour adapter la synchronisation de mouvements à un environnement variable. Elle montre aussi comment l’activité neurale évoquée par un rythme musical complexe est renforcée par les mouvements qui y sont synchronisés. Enfin, elle s’intéresse à ces rythmes neuraux chez des patients ayant des troubles de la marche ou de la conscience.Rhythms are central in human behaviours spanning from locomotion to music performance. In dance, self-sustaining and dynamically adapting neural oscillations entrain to the regular auditory inputs that is the musical beat. This entrainment leads to anticipation of forthcoming sensory events, which in turn allows synchronization of movements to the perceived environment. This dissertation develops novel technical approaches to investigate neural rhythms that are not strictly periodic, such as naturally tempo-varying locomotion movements and rhythms of music. It studies neural responses reflecting the discordance between what the nervous system anticipates and the actual timing of events, and that are critical for synchronizing movements to a changing environment. It also shows how the neural activity elicited by a musical rhythm is shaped by how we move. Finally, it investigates such neural rhythms in patient with gait or consciousness disorders

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201

A Psychophysiological Assessment of the Efficacy of Event-Related Potentials and Electroencephalogram for Adaptive Task Allocation

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocations decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a compensatory tracking task was switched between manual and automatic task modes based upon the participant\u27s EEG. ERPs were also gathered to an auditory, oddball task. Participants in the yoked condition performed the same tasks under the exact sequence of task allocations that participants in the experimental group experienced. The control condition consisted of a random sequence of task allocations that was representative of each participant in the experimental group condition. Therefore, the design allowed a test of whether the performance and workload benefits seen in previous studies using this biocybernetic system were due to adaptive aiding or merely to the increase in task mode allocations. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower tracking errors scores and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of their implications for adaptive automation design

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices