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Brain Computer Interface for Biomedical Applications
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Brain Computer Interface for Biomedical Applications

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3900

Special Issue Editors

Dr. Silvia Francesca Storti

E-Mail Website
Guest Editor
Department of Engineering for Innovation Medicine, University of Verona, 37134 Verona, Italy
Interests: signal and image processing; artificial intelligence; brain connectivity inference and network analysis; brain–computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Silvoni

E-Mail Website
Guest Editor
Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 69120 Heidelberg, Germany
Interests: communication in immobilized persons; learning; cognitive processing; neural plasticity

Special Issue Information

Dear Colleagues,

Over the years, brain–computer Interfaces (BCIs) have shown remarkable potential in revolutionizing biomedical applications, enabling communication, interaction, and motor function restoration for individuals with disabilities. Recently, passive BCIs have emerged, capable of detecting cognitive aspects, such as mental load, attention, and stress.

The successful translation of brain signals into meaningful messages relies on methodological factors, like preprocessing, channel selection methods, feature extraction, and classification algorithms, which remain active areas of research and development. Notably, the use of brain connectivity measures in feature extraction has garnered growing interest for enhancing overall BCI performance. AI methods, including transfer learning approaches, play a pivot role in achieving broad accessibility. They enable the development of personalized rehabilitation plans and decoding complex patterns in biomedical applications.

On the other hand, in clinical and rehabilitative applications, a user-centric approach is vital, emphasizing high flexibility and portability in BCI systems to adapt easily to individual users. Additionally, investigating feedback, rewards, and reinforcement strategies considering emotional and motivational aspects is encouraged, as well as investigations on neural mechanisms underlying transfer-of-benefit in behavioral and/or motor performance induced by plasticity-based BCI systems.

This Special Issue is dedicated at exploring the latest innovations, studies, and developments in the context of BCIs for biomedical applications. It aims to present the state-of-the-art advancements, current challenges, and future trends related to the successful application of BCIs.

Potential topics include, but are not limited to, the following:

  • Advanced BCI technologies for motor and cognitive rehabilitation;
  • BCI applications for communication and assistive technologies;
  • Active and passive BCIs;
  • Development, advantages, and challenges of non-invasive and invasive BCIs;
  • Signal processing of EEG, fMRI, NIRS data for BCIs;
  • Exploring functional brain connectivity and graph analysis in BCIs;
  • Machine learning and deep learning in BCIs;
  • Investigation of transfer learning and domain adaptation in BCIs;
  • Neuro-feedback and BCI;
  • Exploring the link between beneficial BCI-systems effects and neural substrates;
  • Investigating different types and role of feedback/reward in BCI.

Dr. Silvia Francesca Storti
Dr. Stefano Silvoni
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. 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
  • active and passive BCIs
  • electroencephalography (EEG)
  • biomedical signal processing
  • machine learning/deep learning
  • explainable and interpretable AI
  • brain connectivity and graph models
  • classification
  • motor and cognitive rehabilitation
  • BCIs for communication
  • neurofeedback

Published Papers (4 papers)

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Research

16 pages, 1674 KiB  
Article
Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain–Computer Interface Speller under Rapid Serial Visual Presentation
by Ricardo Ron-Angevin, Álvaro Fernández-Rodríguez, Francisco Velasco-Álvarez, Véronique Lespinet-Najib and Jean-Marc André
Sensors 2024, 24(11), 3315; https://doi.org/10.3390/s24113315 - 22 May 2024
Viewed by 489
Abstract
Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific [...] Read more.
Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces—particularly those tinted red—have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems. Full article
(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)
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20 pages, 8600 KiB  
Article
Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application
by Jamila Akhter, Noman Naseer, Hammad Nazeer, Haroon Khan and Peyman Mirtaheri
Sensors 2024, 24(10), 3040; https://doi.org/10.3390/s24103040 - 10 May 2024
Viewed by 751
Abstract
Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature [...] Read more.
Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications. Full article
(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)
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18 pages, 5611 KiB  
Article
Adaptive Time–Frequency Segment Optimization for Motor Imagery Classification
by Junjie Huang, Guorui Li, Qian Zhang, Qingmin Yu and Ting Li
Sensors 2024, 24(5), 1678; https://doi.org/10.3390/s24051678 - 5 Mar 2024
Viewed by 910
Abstract
Motor imagery (MI)-based brain–computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time–frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time–frequency segments. In this study, we [...] Read more.
Motor imagery (MI)-based brain–computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time–frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time–frequency segments. In this study, we propose a novel method for optimizing time–frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time–frequency segments. Our proposed algorithm enables adaptive optimization of EEG time–frequency segments, which is crucial for the development of clinically effective motor rehabilitation. Full article
(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)
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18 pages, 2846 KiB  
Article
Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance
by Ilaria Siviero, Gloria Menegaz and Silvia Francesca Storti
Sensors 2023, 23(17), 7520; https://doi.org/10.3390/s23177520 - 30 Aug 2023
Cited by 2 | Viewed by 1217
Abstract
(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there [...] Read more.
(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system. Full article
(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)
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