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

From Coaching to Neurocoaching: A Neuroscientific Approach during a Coaching Session to Assess the Relational Dynamics between Coach and Coachee-A Pilot Study.

Life transitions represent moments characterized by changes that can profoundly influence individual life trajectories and subjective well-being. Recently, career coaching has become an important method of helping people expand their self-awareness, facilitate personal development, and increase their performance in the school-to-work transition. Although previous studies have confirmed that one of the most important keys to the success of a coaching program is the quality of the relationship between coach and coachee, there is a lack of knowledge regarding how to objectively measure it. In this pilot study, we adopted a neuroscientific approach to introduce objective measures of the relationship between coach and coachee through the phases of a coaching session. A sample of 14 university students and a professional coach participated in career-coaching sessions while their affective states were measured by recording brain (EEG) and physiological (Skin conductance) activity. Electroencephalographic indicators of valence, arousal, and engagement showed differences between session phases, highlighting the possibility of a neurophysiological measurement of relational dynamics. Our results provide initial evidence that neurophysiological activity can be considered a way to understand differences in the coach-coachee relationship, thereby providing information on the effectiveness of coaching interventions and facilitating a better life transition from school to work

Using neurophysiological signals that reflect cognitive or affective state: Six recommendations to avoid common pitfalls

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic “Using neurophysiological signals that reflect cognitive or affective state” we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently “cheating” with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications

Hyperbrain features of team mental models within a juggling paradigm: a proof of concept

Background Research on cooperative behavior and the social brain exists, but little research has focused on real-time motor cooperative behavior and its neural correlates. In this proof of concept study, we explored the conceptual notion of shared and complementary mental models through EEG mapping of two brains performing a real-world interactive motor task of increasing difficulty. We used the recently introduced participative “juggling paradigm,” and collected neuro-physiological and psycho-social data. We were interested in analyzing the between-brains coupling during a dyadic juggling task, and in exploring the relationship between the motor task execution, the jugglers’skill level and the task difficulty. We also investigated how this relationship could be mirrored in the coupled functional organization of the interacting brains. Methods To capture the neural schemas underlying the notion of shared and complementary mental models, we examined the functional connectivity patterns and hyperbrain features of a juggling dyad involved in cooperative motor tasks of increasing difficulty. Jugglers’ cortical activity was measured using two synchronized 32-channel EEG systems during dyadic juggling performed with 3, 4, 5 and 6 balls. Individual and hyperbrain functional connections were quantified through coherence maps calculated across all electrode pairs in the theta and alpha bands (4–8 and 8–12 Hz). Graph metrics were used to typify the global topology and efficiency of the functional networks for the four difficulty levels in the theta and alpha bands. Results Results indicated that, as task difficulty increased, the cortical functional organization of the more skilled juggler became progressively more segregated in both frequency bands, with a small-world organization in the theta band during easier tasks, indicative of a flow-like state in line with the neural efficiency hypothesis. Conversely, more integrated functional patterns were observed for the less skilled juggler in both frequency bands, possibly related to cognitive overload due to the difficulty of the task at hand (reinvestment hypothesis). At the hyperbrain level, a segregated functional organization involving areas of the visuo-attentional networks of both jugglers was observed in both frequency bands and for the easier task only. Discussion These results suggest that cooperative juggling is supported by integrated activity of specialized cortical areas from both brains only during easier tasks, whereas it relies on individual skills, mirrored in uncorrelated individual brain activations, during more difficult tasks. These findings suggest that task difficulty and jugglers’ personal skills may influence the features of the hyperbrain network in its shared/integrative and complementary/segregative tendencies

Detecting Flow Experiences in Cognitive Tasks - A Neurophysiological Approach

Das Flow-Erlebnis beschreibt einen Zustand vollständiger Aufgabenvertiefung und mühelosen Handelns, der mit Höchstleistungen, persönlichem Wachstum, sowie allgemeinem Wohlbefinden verbunden ist. Für Unternehmen stellen häufigere Flow-Erlebnisse der ArbeitnehmerInnen daher auch eine produktivitäts- und zufriedenheitsfördernde Basis dar. Vor allem da sich aktuell globale Phänomene wie die steigende Nachfrage nach Wissensarbeit und das niedrige Arbeitsengagement zuspitzen, können Unternehmen von einer Förderung von Flow profitieren. Die Unterstützung von Flow stellt allerdings aufgrund der Vielfalt von Arbeitnehmerfertigkeiten, -aufgaben, und -arbeitsplätzen eine komplexe Herausforderung dar. WissensarbeiterInnen stehen dynamischen Aufgaben gegenüber, die diverse Kompetenzen und die Kooperation mit anderen erfordern. Arbeitsplätze werden vielseitiger, indem die Grenzen zwischen ko-präsenten und virtuellen Interaktionen verschwinden. Diese Vielfalt bedeutet, dass eine solide Flow-Förderung nur durch personen-, aufgaben- und situationsunabhängige Ansätze erfolgen kann. Aus diesem Grund werden zunehmend die neurophysiologischen Grundlagen des Flow-Erlebens untersucht. Auf deren Basis könnten adaptive Neuro-Informationssysteme entwickelt werden, die mittels tragbarer Sensorik Flow kontinuierlich erkennen und fördern können. Diese Wissensbasis ist bislang jedoch nur spärlich und in stark fragmentierter Form vorhanden. Für das Individuum existieren lediglich konkurrierende Vorschläge, die noch nicht durch situations- und sensorübergreifende Studien konsolidiert wurden. Für Gruppen existiert noch fast keine Forschung zu neurophysiologischen Flow-Korrelaten, insbesondere keine im Kontext digital-mediierter Interaktionen. In dieser Dissertation werden genau diese Forschungslücken durch die situationsübergreifende Beobachtung von Flow mit tragbaren EKG und EEG Sensoren adressiert. Dabei werden zentrale Grenzen der experimentellen Flow-Forschung berücksichtigt, vor allem die Defizite etablierter Paradigmen zum kontrollierten Hervorrufen von Flow. Indem Erlebnisse in zwei kognitiven Aufgaben und mehreren Manipulationen (von Schwierigkeit, Natürlichkeit, Autonomie und sozialer Interaktion) variiert werden, wird untersucht, wie Flow intensiver hervorgerufen und wie das Erlebnis stabiler über Situationen hinweg beobachtet werden kann. Die Studienergebnisse deuten dabei insgesamt auf ein Flow-Muster von moderater physiologischer Aktivierung und mentaler Arbeitslast, von erhöhter, aufgabenorientierter Aufmerksamkeit und von affektiver Neutralität hin. Vor allem die EEG Daten zeigen ein diagnostisches Potenzial, schwächere von stärkeren Flow-Zuständen unterscheiden zu können, indem optimale und nicht-optimale Aufgabenschwierigkeiten (für Individuen und Gruppen) erkannt werden. Um das Flow-Erleben weiter zu fördern, werden geeignete Wege für zukünftige Forschung abschließend diskutiert

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 165, March 1977

This bibliography lists 198 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1977

Functional brain networks before the onset of psychosis : a prospective fMRI study with graph theoretical analysis

Individuals with an at-risk mental state (ARMS) have a risk of developing a psychotic disorder significantly greater than the general population. However, it is not currently possible to predict which ARMS individuals will develop psychosis from clinical assessment alone. Comparison of ARMS subjects who do, and do not, develop psychosis can reveal which factors are critical for the onset of illness. In the present study, 37 patients with an ARMS were followed clinically at least 24 months subsequent to initial referral. Functional MRI data were collected at the beginning of the follow-up period during performance of an executive task known to recruit frontal lobe networks and to be impaired in psychosis. Graph theoretical analysis was used to compare the organization of a functional brain network in ARMS patients who developed a psychotic disorder following the scan (ARMS-T) to those who did not become ill during the same follow-up period (ARMS-NT) and aged-matched controls. The global properties of each group's representative network were studied (density, efficiency, global average path length) as well as regionally-specific contributions of network nodes to the organization of the system (degree, farness-centrality, betweenness-centrality). We focused our analysis on the dorsal anterior cingulate cortex (ACC), a region known to support executive function that is structurally and functionally impaired in ARMS patients. In the absence of between-group differences in global network organization, we report a significant reduction in the topological centrality of the ACC in the ARMS-T group relative to both ARMS-NT and controls. These results provide evidence that abnormalities in the functional organization of the brain predate the onset of psychosis, and suggest that loss of ACC topological centrality is a potential biomarker for transition to psychosis