Collecting neurophysiological data to investigate users’ cognitive states during game play

This paper explores the potential of collecting neurophysiological data in order to further understand user’s learning experience. The experimental setup involves collecting electroencephalographic signal (EEG) from the brain cortex to infer users’ cognitive state while they played an educational video game designed to support the learning of Newtonian mechanics. Preliminary results suggest that this neuroscience perspective is quite promising in the idea of quantitatively characterizing users’ learning experience. This could be an innovative and promising avenue in general game development or in educational videogame research field

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

EEG Pattern Analysis for Physiological Indicators of Mental Fatigue in Simulated Air Traffic Control Tasks

Objective: This study was designed to identify potential neurophysiologic markers and patterns of mental fatigue among air traffic controllers in their work environment.Background: The monitoring of mental fatigue in air traffic controllers has been of interest as their tasks involve high cognitive workload and are also critical to the safety of the public.Method: High-density electroencephalogram (EEG) was used to record 2-hour long air traffic control studies in eleven participants. Participants were asked to perform realistic tasks in a simulation, to operate a virtual air traffic control system. Fourier Transforms were used to estimate EEG power spectrum, statistical tests were implemented to reveal EEG spatial pattern changes caused by the time-on-task. The concept of mental state transition was introduced to study the development of certain mental states which are related to the mental fatigue. Results: The observation of EEG spectral data over a period of time revealed statistically significant changes spatially localized to central and parietal cortices. Rhythmic EEG activity within theta, alpha, and beta bands indicates transitions among mental states, which appear to be promising indicators for the development of mental fatigue. Mental fatigue indicated by the transition of mental states was estimated to approximately 70 minutes after the time on task. Application: This study can build the foundation to develop promising technologies for real time monitoring of mental fatigue, which will increase public safety and proper human resource planning.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Classification of Affective Data to Evaluate the Level Design in a Role-Playing Videogame

This paper presents a novel approach to evaluate game level design strategies, applied to role playing games. Following a set of well defined guidelines, two game levels were designed for Neverwinter Nights 2 to manipulate particular emotions like boredom or flow, and tested by 13 subjects wearing a brain computer interface helmet. A set of features was extracted from the affective data logs and used to classify different parts of the gaming sessions, to verify the correspondence of the original level aims and the effective results on people emotions. The very interesting correlations observed, suggest that the technique is extensible to other similar evaluation tasks

Determine Task Demand from Brain Activity

Our society demands ubiquitous mobile devices that offer seamless interaction with everybody, everything, everywhere, at any given time. However, the effectiveness of these devices is limited due to their lack of situational awareness and sense for the users ’ needs. To overcome this problem we develop intelligent transparent human-centered systems that sense, analyze, and interpret the user’s needs. We implemented learning approaches that derive the current task demand from the user’s brain activity by measuring the electroencephalogram. Using Support Vector Machines we can discriminate high versus low task demand with an accuracy of 92.2 % in session dependent experiments, 87.1 % in session independent experiments, and 80.0 % in subject independent experiments. To make brain activity measurements less cumbersome, we built a comfortable headband with which we achieve 69 % classification accuracy on the same task.

Robust Models for Operator Workload Estimation

When human-machine system operators are overwhelmed, judicious employment of automation can be beneficial. Ideally, a system which can accurately estimate current operator workload can make better choices when to employ automation. Supervised machine learning models can be trained to estimate workload in real time from operator physiological data. Unfortunately, estimating operator workload using trained models is limited: using a model trained in one context can yield poor estimation of workload in another. This research examines the utility of three algorithms (linear regression, regression trees, and Artificial Neural Networks) in terms of cross-application workload prediction. The study is conducted for a remotely piloted aircraft simulation under several context-switch scenarios -- across two tasks, four task conditions, and seven human operators. Regression tree models were able to cross predict both task conditions of one task type within a reasonable level of error, and could accurately predict workload for one operator when trained on data from the other six. Six physiological input subsets were identified based on method of measurement, and were shown to produce superior cross-application models compared to models utilizing all input features in certain instances. Models utilizing only EEG features show the most potential for decreasing cross application error

Psycho-Physiologically-Based Real Time Adaptive General Type 2 Fuzzy Modelling and Self-Organising Control of Operator's Performance Undertaking a Cognitive Task

—This paper presents a new modelling and control fuzzy-based framework validated with real-time experiments on human participants experiencing stress via mental arithmetic cognitive tasks identified through psycho-physiological markers. The ultimate aim of the modelling/control framework is to prevent performance breakdown in human-computer interactive systems with a special focus on human performance. Two designed modelling/control experiments which consist of carrying-out arithmetic operations of varying difficulty levels were performed by 10 participants (operators) in the study. With this new technique, modelling is achieved through a new adaptive, self-organizing and interpretable modelling framework based on General Type-2 Fuzzy sets. This framework is able to learn in real-time through the implementation of a re-structured performance-learning algorithm that identifies important features in the data without the need for prior training. The information learnt by the model is later exploited via an Energy Model Based Controller that infers adequate control actions by changing the difficulty level of the arithmetic operations in the human-computer-interaction system; these actions being based on the most current psycho-physiological state of the subject under study. The real-time implementation of the proposed modelling and control configurations for the human-machine-interaction under study shows superior performance as compared to other forms of modelling and control, with minimal intervention in terms of model re-training or parameter re-tuning to deal with uncertainties, disturbances and inter/intra-subject parameter variability

Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review

International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service