Brain–Computer Interfaces for Multimodal Human–Computer Interaction

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 5873

Special Issue Editors


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Guest Editor
Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: EEG; brain-computer interface; virtual reality; stroke rehabilitation; machine learning
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Guest Editor
Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
Interests: EEG; brain-computer interface; signal processing; stroke rehabilitation; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Systems and Robotics, Department of Bioengineering, Instituto Superior Técnico,Universidade de Lisboa, Lisboa, Portugal
Interests: neuroimaging; MRI; EEG; neuroscience; biomedical engineering

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore multimodal human–computer interaction (HCI) with the use of brain–computer interfaces (BCIs).

BCIs can be described as communication systems able to establish an alternative pathway between the user’s brain activity and a computer, providing an additional non-muscular channel for communication and control to the external world. This can be achieved either explicitly by allowing users to issue direct commands into devices without physical involvement of any kind (e.g., speller typing, wheelchair control), or implicitly by monitoring a user’s state (e.g., workload level, attention state) to proactively adapt a user-interface or a virtual environment.

Undoubtedly, BCIs have not only been proven to be important tools in the medical domain as either assistive or restorative interfaces but also have introduced a unique form for the human–computer interaction paradigm.

In the last few years, BCIs have progressed as an emerging research area in the fields of HCI and interactive systems, primarily due to the introduction of low-cost EEG systems that render BCI technology accessible for non-medical research. Consequently, BCIs provide a wide new range of possibilities in the way users interact with a computer system (e.g., neuroadaptive interfaces). However, major challenges must still be tackled for BCI systems to mature into an established communication medium for effective human–computer interaction.

The goal of this Special Issue is to create an understanding of the current capabilities of BCIs in multimodal human–computer interaction, highlight current and future applications, and identify and tackle the most relevant technical challenges.

We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles including but not limited to:

  • Improving the usability or functionality of BCIs;
  • Realistic, immersive, and/or multisensory scenarios that make use of a BCI;
  • Methodological and technical advancements related to multimodal interaction and BCI;
  • Increasing the communication bandwidth with hybrid BCIs (two or more modalities) or shared-control BCIs (combination of two or more BCI paradigms);
  • Health applications related to assistive BCIs for communication and control of restorative BCIs for rehabilitation;
  • Non-health applications in interaction, neuroadaptive interfaces, entertainment, education, training, or other areas that result from the use of BCIs.

Dr. Athanasios Vourvopoulos
Dr. Serafeim Perdikis
Dr. Patricia Figueiredo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Multimodal Technologies and Interaction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • brain–computer interfaces
  • human–computer interaction
  • multimodal interaction
  • neuroadaptive interfaces
  • electroencephalography
  • virtual reality
  • augmented reality

Published Papers (1 paper)

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Research

21 pages, 865 KiB  
Article
Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training
by Léa Pillette, Bernard N’Kaoua, Romain Sabau, Bertrand Glize and Fabien Lotte
Multimodal Technol. Interact. 2021, 5(3), 12; https://doi.org/10.3390/mti5030012 - 19 Mar 2021
Cited by 13 | Viewed by 4355
Abstract
By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns [...] Read more.
By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback. Full article
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