BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded Future BNCI CSA that started in 2010 and produced a roadmap for a shorter time period. We present how we, a consortium of the main European BCI research groups as well as companies and end user representatives, expect to tackle the problem of designing a roadmap for BCI research. In this paper, we define the field with its recent developments, in particular by considering publications and EU-funded research projects, and we discuss how we plan to involve research groups, companies, and user groups in our effort to pave the way for useful and fruitful EU-funded BCI research for the next ten years

The future in brain/neural computer interaction: Horizon 2020

The main objective of this roadmap is to provide a global perspective on the BCI field now and in the future. For readers not familiar with BCIs, we introduce basic terminology and concepts. We discuss what BCIs are, what BCIs can do, and who can benefit from BCIs. We illustrate our arguments with use cases to support the main messages. After reading this roadmap you will have a clear picture of the potential benefits and challenges of BCIs, the steps necessary to bridge the gap between current and future applications, and the potential impact of BCIs on society in the next decade and beyond

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeEC/FP7/625991/EU/Hyperscanning 2.0 Analyses of Multimodal Neuroimaging Data: Concept, Methods and Applications/HYPERSCANNING 2.0DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

A TECHNOLOGY ASSESSMENT OF BRAIN-COMPUTER INTERFACES. BRIDGING PRESENT AND FUTURE WITH A HUMAN-CENTERED PERSPECTIVE

Technology assessment is a systematic approach used to scientifically investigate the conditions and consequences of technology and technicization while determining its social evaluation. This research focuses on the evaluation of an emerging technology, Brain-Computer Interface (BCI), which enables direct communication between the brain and an external device. As an emerging technology, BCI is in its early stages of research, facing numerous challenges. To address the assessment of BCIs, a method-ology combining Constructive Technology Assessment (CTA) and Foresight within the umbrella con-cept of Future-oriented Technology Analysis (FTA), has been developed and applied. This thesis con-ducts a literature review and applies both structured, open-ended interviews and a survey seeking an-swers to these issues. It explores various social, ethical, legal, and philosophical issues to be addressed in the field of BCIs, both in the present as well as in the future. Understanding the key challenges, de-velopments, and potential future trajectories of this technology is essential to grasp how its applications can offer both opportunities and threats to society at large. The research addresses the concerns of both the Technology Assessment and Brain-Computer Interface communities, offering a comprehensive un-derstanding of how these social, ethical, legal, and philosophical issues may evolve over time. Perspec-tives from various key stakeholders in the BCI field, as well as neurotechnologies in the context of as-sistive technologies, are examined, providing valuable insights for further research in this area.A avaliação de tecnologia é uma abordagem sistemática usada para investigar cientificamente as condições e consequências da tecnologia e da tecnicização, ao mesmo tempo que determina sua avaliação social. Esta pesquisa concentra-se na avaliação de uma tecnologia emergente, a Interface Cérebro-Computador (BCI), que possibilita a comunicação direta entre o cérebro e um dispositivo externo. Como tecnologia emergente, a BCI está em seus estágios iniciais de pesquisa, enfrentando inúmeros desafios. Para abordar a avaliação das BCIs, foi desenvolvida e aplicada uma metodologia que combina a Avaliação Construtiva de Tecnologia (CTA) e a Prospectiva, dentro do conceito geral de Análise de Tecnologia Orientada para o Futuro (FTA). Esta tese realiza uma revisão de literatura e aplica tanto entrevistas estruturadas e abertas quanto um questionário na busca por respostas para estas questões. Ela explora várias questões sociais, éticas, legais e filosóficas a serem abordadas no campo das BCIs, tanto no presente como no futuro. Compreender os principais desafios, desenvolvimentos e possíveis trajetórias futuras dessa tecnologia é essencial para compreender como suas aplicações podem oferecer oportunidades e ameaças à sociedade em geral. A pesquisa aborda as preocupações das comunidades de Avaliação de Tecnologia e Interface Cérebro-Computador, oferecendo uma compreensão abrangente de como essas questões sociais, éticas, legais e filosóficas podem evoluir ao longo do tempo. Perspectivas de diversos atores-chave no campo de BCI, bem como neurotecnologias no contexto de tecnologias assistivas, são examinadas, fornecendo informações valiosas para pesquisas futuras nessa área

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

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Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user is able to actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance, making the system safer and more reliable. In this regard, the robot can exploit two maps of the environment, one for localization and one for navigation, and both can be used also by the BCI user to watch the position of the robot while it is moving. As demonstrated by the experimental results, the user's cognitive workload is reduced, decreasing the number of commands necessary to complete the task and helping him/her to keep attention for longer periods of time.Comment: Accepted in the Proceedings of the 2018 IEEE International Conference on Robotics and Automatio