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Advanced Sensors in Brain–Computer Interfaces

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 178

Special Issue Editor


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Guest Editor
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: brain–computer interface (BCI); supervision of complex systems; fault detection and diagnosis; improvement in electrical distribution; reliability evaluation of distribution systems; microgrids and smartgrids; integration of renewable energies in distribution systems
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Special Issue Information

Dear Colleagues,

This Special Issue reviews the latest advances in BCI research, highlighting cutting-edge methodologies, novel applications, and interdisciplinary approaches that push the boundaries of what is possible. Contributions from leading experts address key topics such as brain signal acquisition, real-time processing techniques, machine learning algorithms, and the integration of BCIs with emerging technologies such as artificial intelligence and robotics. By bringing together diverse perspectives, this publication aims to foster collaboration and inspire future advances in this rapidly evolving field.

Prof. Dr. Eduardo Quiles
Guest Editor

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Keywords

  • brain–computer interface (BCI)
  • signal processing
  • brain signal acquisition
  • cognitive enhancement
  • machine learning
  • human–machine interaction
  • neuroprosthetics
  • EEG (electroencephalography)
  • neurofeedback
  • artificial intelligence
  • real-time processing
  • brain connectivity
  • neurorehabilitation

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Published Papers (1 paper)

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Research

30 pages, 1638 KiB  
Article
Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It
by Bastian Ilsø Hougaard, Hendrik Knoche, Mathias Sand Kristensen and Mads Jochumsen
Sensors 2025, 25(9), 2742; https://doi.org/10.3390/s25092742 - 26 Apr 2025
Viewed by 69
Abstract
Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain–computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users’ experience of virtual [...] Read more.
Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain–computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users’ experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients’ perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal. Full article
(This article belongs to the Special Issue Advanced Sensors in Brain–Computer Interfaces)
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