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Advanced Sensors in Brain–Computer Interfaces

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Prof. Dr. Eduardo Quiles

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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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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
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30 pages, 1638 KiB

Open AccessArticle

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

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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 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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35 pages, 1765 KiB

Open AccessReview

The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications

by Alexandra Stefania Mihai (Ungureanu), Oana Geman, Roxana Toderean, Lucas Miron and Sara SharghiLavan

Sensors 2025, 25(11), 3321; https://doi.org/10.3390/s25113321 - 25 May 2025

Viewed by 630

Abstract

Electroencephalography (EEG) remains an essential method for monitoring brain activity, but the limitations of conventional systems due to the complexity of installation and lack of portability have led to the introduction and development of in-ear EEG technology. In-ear EEG is an emerging method of recording electrical activity in the brain and is an innovative concept that offers multiple advantages both from the point of view of the device itself, which is easily portable, and from the user’s point of view, who is more comfortable with it, even in long-term use. One of the fundamental components of this type of device is the electrodes used to capture the EEG signal. This innovative method allows bioelectrical signals to be captured through electrodes integrated into an earpiece, offering significant advantages in terms of comfort, portability, and accessibility. Recent studies have demonstrated that in-ear EEG can record signals qualitatively comparable to scalp EEG, with an optimized signal-to-noise ratio and improved electrode stability. Furthermore, this review provides a comparative synthesis of performance parameters such as signal-to-noise ratio (SNR), common-mode rejection ratio (CMRR), signal amplitude, and comfort, highlighting the strengths and limitations of in-ear EEG systems relative to conventional scalp EEG. This study also introduces a visual model outlining the stages of technological development for in-ear EEG, from initial research to clinical and commercial deployment. Particular attention is given to current innovations in electrode materials and design strategies aimed at balancing biocompatibility, signal fidelity, and anatomical adaptability. This article analyzes the evolution of EEG in the ear, briefly presents the comparative aspects of EEG—EEG in the ear from the perspective of the electrodes used, highlighting the advantages and challenges of using this new technology. It also discusses aspects related to the electrodes used in EEG in the ear: types of electrodes used in EEG in the ear, improvement of contact impedance, and adaptability to the anatomical variability of the ear canal. A comparative analysis of electrode performance in terms of signal quality, long-term stability, and compatibility with use in daily life was also performed. The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain–computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. Despite its considerable potential, the widespread deployment of in-ear EEG faces challenges such as anatomical variability of the ear canal, optimization of ergonomics, and reduction in motion artifacts. Future research aims to improve device design for long-term monitoring, integrate advanced signal processing algorithms, and explore applications in neurorehabilitation and early diagnosis of neurodegenerative diseases. Full article

(This article belongs to the Special Issue Advanced Sensors in Brain–Computer Interfaces)

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