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      Eyes on the road: brain computer interfaces and cognitive distraction in traffic

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          Abstract

          Novel wearable neurotechnology is able to provide insight into its wearer's cognitive processes and offers ways to change or enhance their capacities. Moreover, it offers the promise of hands-free device control. These brain-computer interfaces are likely to become an everyday technology in the near future, due to their increasing accessibility and affordability. We, therefore, must anticipate their impact, not only on society and individuals broadly but also more specifically on sectors such as traffic and transport. In an economy where attention is increasingly becoming a scarce good, these innovations may present both opportunities and challenges for daily activities that require focus, such as driving and cycling. Here, we argue that their development carries a dual risk. Firstly, BCI-based devices may match or further increase the intensity of cognitive human-technology interaction over the current hands-free communication devices which, despite being widely accepted, are well-known for introducing a significant amount of cognitive load and distraction. Secondly, BCI-based devices will be typically harder than hands-free devices to both visually detect (e.g., how can law enforcement check when these extremely small and well-integrated devices are used?) and restrain in their use (e.g., how do we prevent users from using such neurotechnologies without breaching personal integrity and privacy?). Their use in traffic should be anticipated by researchers, engineers, and policymakers, in order to ensure the safety of all road users.

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          Most cited references21

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          Enhanced performance by a hybrid NIRS-EEG brain computer interface.

          Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

            Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
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              Assessing Cognitive Distraction in the Automobile

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                Author and article information

                Contributors
                Journal
                Front Neurogenom
                Front Neurogenom
                Front. Neuroergon.
                Frontiers in Neuroergonomics
                Frontiers Media S.A.
                2673-6195
                26 May 2023
                2023
                : 4
                : 1171910
                Affiliations
                [1] 1Machine Learning, Institute of Cognitive Science, Osnabrück University , Osnabrück, Germany
                [2] 2Department of Artificial Intelligence, Donders Institute for Brain, Cognition and Behavior, Radboud University , Nijmegen, Netherlands
                Author notes

                Edited by: Jason Scott Metcalfe, Humans in Complex Systems, United States

                Reviewed by: Sung Won Lee, University of Arizona, United States

                *Correspondence: Victoria Bosch victoria.bosch@ 123456uos.de
                Article
                10.3389/fnrgo.2023.1171910
                10790900
                38234470
                eafabf46-46e0-472a-beef-300bbc88c402
                Copyright © 2023 Bosch and Mecacci.

                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) and the copyright owner(s) 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.

                History
                : 22 February 2023
                : 11 May 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 36, Pages: 5, Words: 4576
                Funding
                Funded by: Universität Osnabrück, doi 10.13039/501100016379;
                We acknowledge support by Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publishing Fund of Osnabrück University.
                Categories
                Neuroergonomics
                Perspective
                Custom metadata
                Social Neuroergonomics

                bci (brain computer interface),human-machine interaction,neurotechnology,traffic,societal impact of ai,ethics of ai,cognitive distraction

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