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      Cybersicherheit von Gehirn-Computer-Schnittstellen Translated title: Cybersecurity of brain–computer interfaces

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          Abstract

          Gehirn-Computer-Schnittstellen beflügeln die Hoffnung auf übermenschliche Kräfte: Sie versetzen Nutzer in die Lage, Prothesen und sonstige Geräte allein mit ihren Gedanken zu steuern. Je weiter die Entwicklung der neuen Technologie voranschreitet und in marktfähige Produkte mündet, desto sichtbarer rücken auch potenzielle Sicherheitsrisiken in den Fokus. Denn Angriffe auf Gehirn-Computer-Schnittstellen können neurologische Daten erspähen oder Gehirnaktivitäten manipulieren und dadurch verheerende Schäden verursachen. Der Beitrag geht der Frage auf den Grund, wie die Rechtsordnung den Risiken eines Angriffs auf Gehirn-Computer-Schnittstellen bislang begegnet – und wie sie ihnen künftig begegnen sollte.

          Translated abstract

          Brain-computer interfaces inspire visions of superhuman powers, enabling users to control protheses and other devices solely with their thoughts. But the rapid development and commercialization of this technology also brings security risks. Attacks on brain-computer interfaces may cause harrowing consequences for users, from eavesdropping on neurological data to manipulating brain activity. At present, data protection law, the regulation of medical devices, and the new rules on the sale of goods with digital elements all govern aspects of cybersecurity. There are, nevertheless, significant gaps. The article analyzes how the legal system currently addresses the risks of cyberattacks on brain–computer interfaces—and how policymakers could address such risks in the future.

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          Most cited references181

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          Edge Computing: Vision and Challenges

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            Introduction to machine learning for brain imaging.

            Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Novel electrode technologies for neural recordings

              Neural recording electrode technologies have contributed considerably to neuroscience by enabling the extracellular detection of low-frequency local field potential oscillations and high-frequency action potentials of single units. Nevertheless, several long-standing limitations exist, including low multiplexity, deleterious chronic immune responses and long-term recording instability. Driven by initiatives encouraging the generation of novel neurotechnologies and the maturation of technologies to fabricate high-density electronics, novel electrode technologies are emerging. Here, we provide an overview of recently developed neural recording electrode technologies with high spatial integration, long-term stability and multiple functionalities. We describe how these emergent neurotechnologies can approach the ultimate goal of illuminating chronic brain activity with minimal disruption of the neural environment, thereby providing unprecedented opportunities for neuroscience research in the future.
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                Author and article information

                Contributors
                martini@uni-speyer.de
                kemper@foev-speyer.de
                Journal
                Int. Cybersecur. Law Rev.
                International Cybersecurity Law Review
                Springer Fachmedien Wiesbaden (Wiesbaden )
                2662-9720
                2662-9739
                17 March 2022
                17 March 2022
                : 1-53
                Affiliations
                [1 ]GRID grid.448867.1, ISNI 0000 0001 0709 4474, Deutsche Universität für Verwaltungswissenschaften (DUV), ; Speyer, Deutschland
                [2 ]GRID grid.461664.7, ISNI 0000 0001 1091 6758, Deutsches Forschungsinstitut für öffentliche Verwaltung (FÖV), ; Speyer, Deutschland
                Article
                46
                10.1365/s43439-022-00046-x
                8929247
                c91a4ac0-6f0d-4dc1-a98d-56e713e13c13
                © The Author(s) 2022

                Open Access Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden.

                Die in diesem Artikel enthaltenen Bilder und sonstiges Drittmaterial unterliegen ebenfalls der genannten Creative Commons Lizenz, sofern sich aus der Abbildungslegende nichts anderes ergibt. Sofern das betreffende Material nicht unter der genannten Creative Commons Lizenz steht und die betreffende Handlung nicht nach gesetzlichen Vorschriften erlaubt ist, ist für die oben aufgeführten Weiterverwendungen des Materials die Einwilligung des jeweiligen Rechteinhabers einzuholen.

                Weitere Details zur Lizenz entnehmen Sie bitte der Lizenzinformation auf http://creativecommons.org/licenses/by/4.0/deed.de.

                History
                : 11 October 2021
                : 29 January 2022
                Funding
                Funded by: Deutsche Universität für Verwaltungswissenschaften (5169)
                Categories
                Article

                it-sicherheit,medizinprodukte,wearables,datensicherheit,neurotechnologie,it security,medical devices,data security,neurotechnology

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