EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

Brain-Computer Interface for Clinical Purposes : Cognitive Assessment and Rehabilitation

Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients' motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes

Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention

The efficacy of electroencephalography neurofeedback for enhancing episodic memory in healthy and clinical participants: A systematic qualitative review and meta-analysis

Several studies have examined whether electroencephalography neurofeedback (EEG-NF), a self-regulatory technique where an individual receives real-time feedback on a pattern of brain activity that is theoretically linked to a target behaviour, can enhance episodic memory. The aim of this research was to i) provide a qualitative overview of the literature, and ii) conduct a meta-analysis of appropriately controlled studies to determine whether EEG-NF can enhance episodic memory. The literature search returned 46 studies, with 21 studies (44 effect sizes) meeting the inclusion criteria for the meta-analysis. The qualitative overview revealed that, across EEG-NF studies on both healthy and clinical populations, procedures and protocols vary considerably and many studies were insufficiently powered with inadequate design features. The meta-analysis, conducted on studies with an active control, revealed a small-size, significant positive effect of EEG-NF on episodic memory performance (g = 0.31, p = 0.003), moderated by memory modality and EEG-NF self-regulation success. These results are discussed with a view towards optimising EEG-NF training and subsequent benefits to episodic memory

Neurophysiological correlates of psychological attitudes of air traffic controllers during their work

The research proposed in this thesis is part of a European project called NINA (Neurometrics Indicators for Air Traffic Management) funded by Sesar Joint Undertaking, and it involves the participation of Sapienza University of Rome, École Nationale de l’Aviation Civile (ENAC), and Deep Blue srl (Human Factor and Safety Consultant Company). The main goal of the project is to elaborate neurophysiological measurements for real-time assessment and monitoring of the cognitive state in particular professional categories, such as Air Traffic Controllers (ATCOs). The evaluation is performed by using a combination of techniques such as Electroencephalography (EEG), Electrocardiography (EKG) and Electrooculography (EOG), during simulated and realistic working conditions. In the area of ATCOs, the Skill, Rule and Knowledge (S-R-K) taxonomy was developed by Rasmussen to describe the human performance under various circumstances and to integrate a variety of research results coming from human cognition studies (attention, memory, problem solving, decision-making, etc.) under a common framework. It provides a description of human cognition that is functional to the understanding and prediction of behaviour: it specifically deals with how people control their activity and behave in interaction with complex systems. Therefore, by considering the aspect of the cognitive processes in the framework of such taxonomy, it is possible to contextualise them in the work practices. Since to our knowledge there are no corresponding studies in the existing literature, another challenging objective of the project is to develop the SRK concept from a neurophysiological point of view. The focus of the proposed thesis is thus to verify the existence of identifiable neurophysiological features associated to the three levels of cognitive control of behaviour (Skill, Rule and Knowledge), in Air Traffic Management (ATM) context, by using a neurometric able to identify the behaviours of the original taxonomy from a different perspective. To map the neurophysiology of the SRK framework in ATM domain, and to use this methodology, could represent a promising step forward into the analysis of human behaviour, and furthermore, to develop new Human Factors tools able to discriminate the level of operators’ expertise during ecological tasks. In detail, the first part of this work illustrates a brief description of the brain and the Electroencephalographic technique, then an introduction of the NINA project and the literature related to the S-R-K levels of cognitive control are presented. The second section is focused on some additional brain features’ literature and on the experimental phase where several steps were performed as follows: a) the three categories of behaviours were associated with specific cognitive functions (e.g. attention, memory, decision making etc.) already investigated in literature with EEG measurements; b) a link between S-R-K behaviours and expected EEG frequency bands configurations were hypothesized; c) specific events were designed to trigger S, R and K behaviours and integrated into realistic ATM simulations; d) finally, the machine-learning algorithm automatic stop StepWise Linear Discriminant Analysis (asSWLDA) was trained to differentiate the three levels of cognitive control of behaviour by using brain features extracted from the EEG rhythms of different brain areas. Several professional ATCOs from the École Nationale de l’Aviation Civile (ENAC) of Toulouse (France) were involved in the study and the results showed that the classification algorithm was able to discriminate with high reliability the three levels of cognitive control of behaviour during simulated air-traffic scenarios in an ecological ATM environment

Definition of Neurophysiological Indices to Describe and Quantify the Cortical Plasticity Induced by Neuro-Rehabilitation

The general objective of the PhD project was to develop a methodology for the definition and analysis of neurophysiological indices able to provide a stable and reliable measure of changes induced by a rehabilitative intervention in the brain activity and organization, with the aim to: i) provide a neurophysiological description of the modifications subtending a functional recovery; ii) allow the evaluation of the effects of rehabilitation treatments in terms of brain reorganization; iii) describe specific properties in the brain general organization to be correlated with the outcome of the intervention, with possible prognostic/decision support value. For this purpose, the research activity was focused on the development of an approach for the extraction of neurophysiological indices from non-invasive estimation of the cerebral activity and connectivity based on electroencephalographic recordings. Brain activity and its changes in time were investigated at three different interconnected levels: spectral analysis, connectivity estimation and graph theory. For each of these, the state of the art methods were evaluated and methodological advancements were proposed on the basis of open problems presented by the nature of the data and by the clinical problem. Experimental data were acquired from 56 stroke patients subjected to a rehabilitative intervention based on Motor Imagery (MI). A subgroup of randomly selected patients were trained in the MI task with the support of Brain Computer Interface. New spectral and functional indices were defined and computed from EEG recorded during the execution of specific tasks (e.g. motor execution), but also from resting state brain activity, to capture both specific and general brain functional modifications. The application of the developed methods allowed to return a proof of the nature, quality and properties of the brain description and quantitative indices that can be derived from data easily recordable from a wide range of patients

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting

Brain-Computer Interface per riabilitazione motoria e cognitiva

Pazienti con lesioni cerebrali o spinali possono essere affetti da gravi deficit nelle funzioni sensoriali, motorie e comunicative; sono perciò sempre più necessarie tecniche di riabilitazione avanzate, personalizzate e adattative, per limitare i deficit insorti e restituire al paziente una vita il più normale possibile. Negli ultimi decenni, numerosi gruppi di ricerca hanno sviluppato Brain-Computer Interface (BCI) basate sul segnale elettroencefalografico (EEG) con l’obbiettivo di fornire mezzi di comunicazione o riabilitazione motoria funzionale. Tuttavia, le tecnologie BCI hanno un ampio potenziale al di là della sola riabilitazione motoria. Applicazioni dei sistemi BCI in protocolli di riabilitazione cognitiva, ad esempio, hanno conseguito risultati promettenti nella prospettiva di migliorare funzioni quali l’attenzione, l'apprendimento e la memoria in pazienti con disturbi delle funzioni cognitive. In questo lavoro di Tesi si analizzano i principi di funzionamento dei sistemi BCI, a partire dall’acquisizione del segnale elettroencefalografico fino all’estrazione e alla classificazione delle feature del segnale per decodificare intenzioni motorie e processi cognitivi (memoria, attenzione) dell’utente. Viene poi presentata un’analisi della letteratura per quando riguarda gli approcci BCI in riabilitazione sia motoria che cognitiva, prestando particolare attenzione ai metodi utilizzati per l’elaborazione e traduzione del segnale EEG. Sono stati considerati con particolare attenzione studi che valutano gli effetti dell’applicazione di BCI non solo attraverso performance motorie e cognitive ma anche utilizzando tecniche di neuro-imaging avanzate, per indagare possibili cambiamenti nell’organizzazione funzionale della corteccia cerebrale sottostanti i risultati positivi ottenuti. Infine, vengono commentati i vantaggi e le limitazioni di queste tecnologie riabilitative e i problemi ancora aperti

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