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      Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (É cole Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

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          Most cited references 38

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            An introduction to ROC analysis

             Tom Fawcett (2006)
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              A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays.

               Zhang,  Chung,  Oldenburg (1999)
              The ability to identify active compounds (³hits²) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the ³suitability² of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called ³Z-factor,² is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                26 October 2016
                2016
                : 10
                Affiliations
                1Department of Molecular Medicine, Sapienza University of Rome Rome, Italy
                2BrainSigns Co. Ltd, Spin-off Company from Sapienza University of Rome Rome, Italy
                3Neuroelectrical Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS) Rome, Italy
                4Department of Anatomical, Histological, Forensic Medicine and Orthopedic Sciences, Sapienza University of Rome Rome, Italy
                5DeepBlue srl Rome, Italy
                6École Nationale de l'Aviation Civile Toulouse, France
                Author notes

                Edited by: Mikhail Lebedev, Duke University, USA

                Reviewed by: Dongrui Wu, University of Southern California, USA; Aleksandra Vuckovic, University of Glasgow, UK; Anastasios Bezerianos, National University of Singapore, Singapore

                *Correspondence: Pietro Aricò pietro.arico@ 123456uniroma1.it

                †These authors have contributed equally to this work.

                Article
                10.3389/fnhum.2016.00539
                5080530
                Copyright © 2016 Aricò, Borghini, Di Flumeri, Colosimo, Bonelli, Golfetti, Pozzi, Imbert, Granger, Benhacene and Babiloni.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 5, Tables: 1, Equations: 8, References: 71, Pages: 13, Words: 10387
                Funding
                Funded by: Horizon 2020 10.13039/501100007601
                Award ID: 699381
                Award ID: 699379
                Funded by: European Commission 10.13039/501100000780
                Categories
                Neuroscience
                Methods

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