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Electronics
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Recent Advances in Brain–Computer Interfaces and Human–Computer Interaction
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Recent Advances in Brain–Computer Interfaces and Human–Computer Interaction

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4437

Special Issue Editor

Prof. Dr. Sandra Gama

E-Mail Website
Guest Editor
Department at Instituto Superior Técnico (IST), Universidade de Lisboa, 1649-004 Lisboa, Portugal
Interests: HCI (human–computer interaction); human factors in HCI; information visualization; gamification; BCI (brain–computer interfaces)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human–computer interaction (HCI) is a widely researched topic, in which extremely relevant contributions over the years have significantly advanced the state of the art.

When designing an interface, it is important to consider that not all humans respond in the same way to the same stimuli. In fact, individual differences play a role of utmost importance in the way humans respond to an interface. Acquiring knowledge on our response is, thus, one potential way toward effectively improving interaction.

Brain–computer interfaces (BCIs) provide the means to interpret the human response to an interface through signal acquisition and analysis, as an alternative or complement to interaction and traditional evaluation strategies, such as questionnaires. Hence, it has shown its value not only in evaluating user interfaces, but also in providing the means for further personalization.

We are pleased to invite you to submit a paper to be published in this issue, integrated in the Electronics journal, an open access journal on the science of electronics and its applications published semi-monthly online by MDPI. This Special Issue aims to advance the state of art on BCI and relevant applications in HCI. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: brain–computer interfaces in human–computer interaction, BCI signal acquisition and interpretation, BCI implementations for HCI, and BCI for interface evaluation.

We look forward to receiving your contributions.

Prof. Dr. Sandra Gama
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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
  • brain-machine interfaces
  • human-computer interaction
  • BCI implementations for HCI
  • BCI for interface evaluation

Published Papers (2 papers)

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Research

14 pages, 693 KiB  
Article
A Novel Turbo Detector Design for a High-Speed SSVEP-Based Brain Speller
by Changkai Tong, Huali Wang and Jun Cai
Electronics 2022, 11(24), 4231; https://doi.org/10.3390/electronics11244231 - 19 Dec 2022
Viewed by 1121
Abstract
The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in [...] Read more.
The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in this study to resolve this issue. The turbo detector uses the filter bank canonical correlation analysis (FBCCA) as the first-stage detector and then utilizes the soft information generated by the first-stage detector and the pool of identified data generated during use to complete the second-stage detection. This strategy allows for rapid performance improvements as the data pool size increases. A standard benchmark dataset was used to evaluate the performance of the proposed method. The results show that the turbo detector can achieve an average ITR of 130 bits/min, which is about 8% higher than FBCCA. As the size of the data pool increases, the ITR of the turbo detector could be further improved. Full article
(This article belongs to the Special Issue Recent Advances in Brain–Computer Interfaces and Human–Computer Interaction)
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23 pages, 3497 KiB  
Article
Exploring the Ability to Classify Visual Perception and Visual Imagery EEG Data: Toward an Intuitive BCI System
by Sunghan Lee, Sehyeon Jang and Sung Chan Jun
Electronics 2022, 11(17), 2706; https://doi.org/10.3390/electronics11172706 - 29 Aug 2022
Cited by 4 | Viewed by 2481
Abstract
Providing an intuitive interface for the actual use of brain–computer interface (BCI) can increase BCI users’ convenience greatly. We explored the possibility that visual imagery can be used as a paradigm that may constitute a more intuitive, active BCI. To do so, electroencephalography [...] Read more.
Providing an intuitive interface for the actual use of brain–computer interface (BCI) can increase BCI users’ convenience greatly. We explored the possibility that visual imagery can be used as a paradigm that may constitute a more intuitive, active BCI. To do so, electroencephalography (EEG) data were collected during visual perception and imagery experiments. Three image categories (object, digit, shape) and three different images per category were used as visual stimuli. EEG data from seven subjects were used in this work. Three types of visual perception/imagery EEG data were preprocessed for classification: raw time series data; time–frequency maps; and common spatial pattern (CSP). Five types of classifiers (EEGNet, 1D convolutional neural network (CNN), MultiRocket, MobileNet, support vector machine (SVM)) were applied to each applicable data type among the three preprocessed types. Thus, we investigated the feasibility of classifying three-category or nine-class visual perception/imagery over various classifiers and preprocessed data types. We found that the MultiRocket network showed the best classification performance: yielding approximately 57.02% (max 63.62%) for three-category classification in visual perception and approximately 46.43% (max 71.38%) accuracy for three-category classification in visual imagery. However, no meaningfully improved performance was achieved in the nine-class classification in either visual perception or imagery, although visual perception yielded slightly higher accuracy than visual imagery. From our extensive investigation, we found that visual perception and visual imagery data may be classified; however, it is somewhat doubtful whether either may be applicable to an actual BCI system. It is believed that introducing better-designed advanced deep learning networks together with more informative feature extractions may improve the performance of EEG visual perception/imagery classifications. In addition, a more sophisticated experimental design paradigm may enhance the potential to achieve more intuitive visual imagery BCI. Full article
(This article belongs to the Special Issue Recent Advances in Brain–Computer Interfaces and Human–Computer Interaction)
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