Symmetry and Asymmetry in Human–Computer Interaction for Medical Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1690

Special Issue Editors


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Guest Editor
School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
Interests: neural engineering; brain-computer interface; neural interfaces; neurorehabilitation; cognitive neurodynamics

E-Mail Website
Guest Editor
School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
Interests: causal artificial intelligence; complex event processing; artificial intelligence of things; medical embedded systems

E-Mail Website
Guest Editor
School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
Interests: human-computer interaction; augmented reality; mixed reality; game design and development

Special Issue Information

Dear Colleagues,

We extend an invitation for submissions to a forthcoming Special Issue of Symmetry titled “Symmetry and Asymmetry in Human–Computer Interaction for Medical Applications”. This Special Issue will focus on the critical role of human–computer interactions (HCI) in the design and implementation of medical technologies, encompassing areas such as diagnostic interfaces, surgical robotics, rehabilitation devices, and assistive systems. The full scope of the Special Issue is available for your review.

In medical environments, which are inherently high-stakes, the principles of symmetry and asymmetry significantly influence perception, cognition, and overall usability. This Special Issue aims to explore the profound impact of symmetry and asymmetry on HCI design, testing, and application within diverse medical fields. Given the rapid advancements in technologies like brain–computer interfaces, robotic surgery, telemedicine, and personalized rehabilitation, medical practices are undergoing substantial transformation. Consequently, a comprehensive understanding of how symmetry and its disruption affect design and performance is paramount, as these factors directly impact information processing, accessibility, usability, and patient safety. This Issue aims to provide critical insights into how these elements shape user experience and clinical outcomes, ultimately fostering the development of more effective, user-centric, and patient-centered medical technologies.

We encourage the submission of both original research articles and comprehensive review articles. Topics of interest include, but are not limited to, the following:

  • Symmetry and asymmetry in medical interface and device design;
  • Brain–computer interfaces (BCIs) for communication and rehabilitation;
  • Symmetrical visual stimuli and their effects on patient interaction;
  • Human-centered design in assistive and clinical technologies;
  • Usability and ergonomics in healthcare HCI systems;
  • Visual perception and cognitive processing in medical user interfaces;
  • Adaptive or personalized HCI systems for patients with impairments.

Dr. Yunyong Punsawad
Dr. Bukhoree Sahoh
Dr. Charlee Kaewrat
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. Symmetry 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 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

  • human–computer interaction
  • medical interface design
  • symmetry
  • asymmetry
  • brain–computer interface (BCI)
  • healthcare technology
  • assistive systems
  • rehabilitation technology
  • visual perception
  • usability

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Published Papers (1 paper)

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Research

20 pages, 1455 KB  
Article
Decoding Self-Imagined Emotions from EEG Signals Using Machine Learning for Affective BCI Systems
by Charoenporn Bouyam, Nannaphat Siribunyaphat, Bukhoree Sahoh and Yunyong Punsawad
Symmetry 2025, 17(11), 1868; https://doi.org/10.3390/sym17111868 (registering DOI) - 4 Nov 2025
Abstract
Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subsequent emotional imagery, involving six positive and six negative emotions across two- or [...] Read more.
Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subsequent emotional imagery, involving six positive and six negative emotions across two- or four-class valence and arousal categories. Machine learning (ML) techniques were applied to interpret these self-generated emotions from electroencephalogram (EEG) signals. Experiments were conducted to observe brain activity and validate the proposed feature and classification algorithms. The results showed that absolute beta power features computed from power spectral density (PSD) across EEG channels consistently achieved the highest classification accuracy for all emotion categories with the K-nearest neighbors (KNN) algorithm, while alpha–beta ratio features also contributed. The nonlinear parametric ML models achieved high effectiveness; the K-nearest neighbor (KNN) classifier performed best in detecting neutral states, while the artificial neural network (ANN) achieved balanced accuracy across emotional stages. The proposed system supports the use of the hybrid emotion induction paradigm and PSD-derived EEG features to develop reliable, subject-independent affective BCI systems. In future work, we will expand the datasets, employ advanced feature extraction and deep learning models, integrate multi-modal signals, and validate the proposed approaches across broader populations. Full article
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