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Brain Computer Interface for Biomedical Applications

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 15720

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Dr. Silvia Francesca Storti

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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

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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

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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

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Published Papers (8 papers)

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22 pages, 6444 KiB

Open AccessArticle

A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs

by Haiqin Xu, Shahzada Ali Hassan, Waseem Haider, Youchao Sun and Xiaojun Yu

Sensors 2025, 25(7), 2134; https://doi.org/10.3390/s25072134 - 28 Mar 2025

Viewed by 403

Abstract

Electroencephalogram (EEG) signal analysis is crucial for understanding neural activity and advancing diagnostics in neurology. However, traditional signal decomposition (SD) techniques are hindered by two critical issues, mode mixing and mode aliasing, that compromise the quality of the decomposed signal. These challenges result in poor signal integrity, which significantly affects the accuracy of subsequent EEG interpretations and classifications. As EEG analysis is widely used in diagnosing conditions such as epilepsy, brain injuries, and sleep disorders, the impact of these shortcomings can be far-reaching, leading to misdiagnoses or delayed treatments. Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. By integrating advanced feature selection techniques and classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and feature-weighted k-nearest neighbors (FWKNN), this approach offers a significant improvement in classification accuracy, with SVM achieving up to 99.99% accuracy in the 18-channel EEG setup with a standard deviation of 0.25. The results demonstrate that FS-VMD can address the critical issues of mode mixing and aliasing, providing a more accurate and efficient solution for EEG signal analysis and diagnostics. Full article

(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)

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24 pages, 5889 KiB

Open AccessArticle

Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)

by Ana Sophia Angulo Medina, Maria Isabel Aguilar Bonilla, Ingrid Daniela Rodríguez Giraldo, John Fernando Montenegro Palacios, Danilo Andrés Cáceres Gutiérrez and Yamil Liscano

Sensors 2024, 24(22), 7125; https://doi.org/10.3390/s24227125 - 6 Nov 2024

Cited by 2 | Viewed by 4684

Abstract

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups. Full article

(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)

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14 pages, 2977 KiB

Open AccessArticle

The Development of a Multicommand Tactile Event-Related Potential-Based Brain–Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator

by Manorot Borirakarawin, Nannaphat Siribunyaphat, Si Thu Aung and Yunyong Punsawad

Sensors 2024, 24(19), 6378; https://doi.org/10.3390/s24196378 - 1 Oct 2024

Viewed by 1506

Abstract

A tactile event-related potential (ERP)-based brain–computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain–computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices. Full article

(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)

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16 pages, 1674 KiB

Open AccessArticle

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 1228

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 1769

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1788

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 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

Open AccessArticle

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 7 | Viewed by 1878

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 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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18 pages, 766 KiB

Open AccessPerspective

Brain Function, Learning, and Role of Feedback in Complete Paralysis

by Stefano Silvoni, Chiara Occhigrossi, Marco Di Giorgi, Dorothée Lulé and Niels Birbaumer

Sensors 2024, 24(19), 6366; https://doi.org/10.3390/s24196366 - 30 Sep 2024

Viewed by 1246

Abstract

The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental–clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain–computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought–action–consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the ‘extinction of thinking’ theory and to identify the actual barriers preventing communication in these patients. Full article

(This article belongs to the Special Issue Brain Computer Interface for Biomedical Applications)

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