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Combining Brain-Computer Interfaces and Assistive Biosensing Technologies
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Combining Brain-Computer Interfaces and Assistive Biosensing Technologies

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1853

Special Issue Editor

Dr. Chunxiu Yu

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
Interests: neuroengineering; brain stimulation (optogenetic and electrical); neural signal processing; brain-computer interface; brain and behavior (parkinson's disease, depression, stroke, and addiction)

Special Issue Information

Dear Colleagues,

The integration of Brain–Computer Interfaces (BCIs) with assistive biosensing technologies represents a frontier in the development of advanced medical and assistive devices. This Special Issue aims to explore the synergies between these fields, highlighting innovative research, technological advancements, and clinical applications including pivotal work involving animal models. Our goal is to provide a comprehensive overview of how these technologies enhance human capabilities, improve quality of life for individuals with disabilities, and open new avenues for medical diagnostics and treatment.

 Potential areas of interest include, but are not limited to, the following:

  • BCI Designs, Implementations and Integration with Assistive Technologies: (1) Development of novel BCIs for real-time control of assistive devices. (2) Advances in non-invasive and invasive BCI technologies.
  • Biosensing Technologies: (1) Development of advanced biosensors for real-time physiological monitoring. (2) Integration of biosensors with BCIs for adaptive feedback systems. (3) Wearable biosensing devices for continuous health monitoring.
  • Preclinical Research and Clinical Applications: (1) Use of animal models to test and refine BCI and biosensing technologies. (2) Insights gained from preclinical studies on neural interfacing and biosensing. (3) Clinical trials and user studies evaluating the efficacy of integrated technologies.
  • Cross-Disciplinary Approaches: (1) Computational models and simulations for system optimization. (2) Data analytics and machine learning applications in BCI and biosensing integration. 

Dr. Chunxiu Yu
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 interface (BCI)
  • assistive technology
  • biosensing
  • machine learning
  • adaptive systems
  • wearable sensors

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Published Papers (2 papers)

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Research

12 pages, 3303 KiB  
Article
Comparison of Subdural and Intracortical Recordings of Somatosensory Evoked Responses
by Felipe Rettore Andreis, Suzan Meijs, Thomas Gomes Nørgaard dos Santos Nielsen, Taha Al Muhamadee Janjua and Winnie Jensen
Sensors 2024, 24(21), 6847; https://doi.org/10.3390/s24216847 - 25 Oct 2024
Viewed by 493
Abstract
Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. [...] Read more.
Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. This study aimed to compare somatosensory evoked potentials (SEPs) through the lenses of a µECoG and an intracortical microelectrode array (MEA). The electrodes were implanted in the pig’s primary somatosensory cortex, while SEPs were generated by applying electrical stimulation to the ulnar nerve. The SEP amplitude, signal-to-noise ratio (SNR), power spectral density (PSD), and correlation structure were analysed. Overall, SEPs resulting from MEA recordings had higher amplitudes and contained significantly more spectral power, especially at higher frequencies. However, the SNRs were similar between the interfaces. These results demonstrate the feasibility of using µECoG to decode SEPs with wide-range applications in physiology monitoring and brain–computer interfaces. Full article
(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)
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18 pages, 8360 KiB  
Article
A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network
by Wenlong Hu, Bowen Ji and Kunpeng Gao
Sensors 2024, 24(16), 5215; https://doi.org/10.3390/s24165215 - 12 Aug 2024
Viewed by 1067
Abstract
The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper [...] Read more.
The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals. Full article
(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)
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