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Advances in Brain–Computer Interfaces and Sensors
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Advances in Brain–Computer Interfaces and Sensors

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3026

Special Issue Editor

Prof. Dr. Luzheng Bi

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Interests: brain–computer interfaces; intelligent human–computer interaction and control; brain–controlled robots and vehicles; brain–machine shared control

Special Issue Information

Dear Colleagues,

Brain–computer interfaces have emerged as a groundbreaking technology that establishes a direct communication pathway between the human brain and external devices, enabling individuals to control and interact with various systems using their brain activity. Sensors play a crucial role in acquiring and interpreting brain signals, facilitating the seamless integration of BCIs into everyday life.

This Special Issue invites researchers and practitioners to contribute original research papers, reviews, case studies, and application-focused articles covering a wide range of topics related to advances in brain–computer interfaces and sensors, including, but not limited to:

  • Novel sensor technologies for brain signal acquisition and analysis;
  • Signal processing and machine learning techniques for brain signal interpretation;
  • Wearable and non-invasive sensors for brain–computer interfaces;
  • Brain–machine shared control for enhanced BCI system performance;
  • Multimodal sensor fusion for enhanced BCI performance;
  • Real-time feedback and control mechanisms in BCIs;
  • Applications of BCIs in healthcare, rehabilitation, and assistive technology;
  • Ethical considerations and user experience in BCI systems;
  • Brain–computer interface integration with virtual reality and augmented reality;
  • Neurofeedback and neurostimulation techniques in BCIs.

Authors are encouraged to present their original research contributions, experimental results, and practical applications related to advances in brain–computer interfaces and sensors. All submissions will undergo a rigorous peer-review process to ensure the publication of high-quality and impactful research.

Prof. Dr. Luzheng Bi
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. Sensors 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 2600 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
  • sensors
  • wearable
  • healthcare
  • rehabilitation
  • assistive technology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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15 pages, 1831 KiB  
Article
Exploring the Feasibility of Bidirectional Control of Beta Oscillatory Power in Healthy Controls as a Potential Intervention for Parkinson’s Disease Movement Impairment
by Krithika Anil, Giorgio Ganis, Jennifer A. Freeman, Jonathan Marsden and Stephen D. Hall
Sensors 2024, 24(16), 5107; https://doi.org/10.3390/s24165107 - 6 Aug 2024
Viewed by 595
Abstract
Neurofeedback (NF) is a promising intervention for improvements in motor performance in Parkinson’s disease. This NF pilot study in healthy participants aimed to achieve the following: (1) determine participants’ ability to bi-directionally modulate sensorimotor beta power and (2) determine the effect of NF [...] Read more.
Neurofeedback (NF) is a promising intervention for improvements in motor performance in Parkinson’s disease. This NF pilot study in healthy participants aimed to achieve the following: (1) determine participants’ ability to bi-directionally modulate sensorimotor beta power and (2) determine the effect of NF on movement performance. A real-time EEG-NF protocol was used to train participants to increase and decrease their individual motor cortex beta power amplitude, using a within-subject double-blind sham-controlled approach. Movement was assessed using a Go/No-go task. Participants completed the NASA Task Load Index and provided verbal feedback of the NF task difficulty. All 17 participants (median age = 38 (19–65); 10 females) reliably reduced sensorimotor beta power. No participant could reliably increase their beta activity. Participants reported that the NF task was challenging, particularly increasing beta. A modest but significant increase in reaction time correlated with a reduction in beta power only in the real condition. Findings suggest that beta power control difficulty varies by modulation direction, affecting participant perceptions. A correlation between beta power reduction and reaction times only in the real condition suggests that intentional beta power reduction may shorten reaction times. Future research should examine the minimum beta threshold for meaningful motor improvements, and the relationship between EEG mechanisms and NF learning to optimise NF outcomes. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces and Sensors)
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10 pages, 1749 KiB  
Article
The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection
by Mads Jochumsen, Emma Rahbek Lavesen, Anne Bruun Griem, Caroline Falkenberg-Andersen and Sofie Kirstine Gedsø Jensen
Sensors 2024, 24(12), 4030; https://doi.org/10.3390/s24124030 - 20 Jun 2024
Viewed by 689
Abstract
Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. [...] Read more.
Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces and Sensors)
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36 pages, 384 KiB  
Systematic Review
Brain Neuroplasticity Leveraging Virtual Reality and Brain–Computer Interface Technologies
by Athanasios Drigas and Angeliki Sideraki
Sensors 2024, 24(17), 5725; https://doi.org/10.3390/s24175725 - 3 Sep 2024
Viewed by 1220
Abstract
This study explores neuroplasticity through the use of virtual reality (VR) and brain–computer interfaces (BCIs). Neuroplasticity is the brain’s ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory [...] Read more.
This study explores neuroplasticity through the use of virtual reality (VR) and brain–computer interfaces (BCIs). Neuroplasticity is the brain’s ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces and Sensors)
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