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Advances in Principles, Methods and Applications of Brain-Computer Interaction

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

Deadline for manuscript submissions: 25 March 2025 | Viewed by 8509

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Special Issue Editors

Dr. Serafeim Perdikis

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Guest Editor

Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
Interests: EEG; brain-computer interface; signal processing; stroke rehabilitation; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Athanasios Vourvopoulos

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Guest Editor

Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: EEG; brain-computer interface; virtual reality; stroke rehabilitation; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer interfaces (BCIs) represent a continuously growing research field that originated in an attempt to enable subjects with severe neuromuscular disorders to communicate and interact with the world around them. Advances in the capabilities of sensors, computation devices, and wireless technologies, as well as in signal processing, machine learning and neuroscience methods have expanded the BCI concept, and it is now subject to investigation in a wide range of fields such as remote healthcare, industry, marketing, education, and gaming. Recently, the use of BCI technology in other aspects of daily life, including mental load management, decision making, neuro-marketing, and gaming, has been explored. As the aspiration is that BCI technology will gradually move towards use in practical applications, the need for more reliable and robust solutions for detecting user intent is, in the current landscape, as urgent and important as it ever has been. The battle to deploy BCI technology in real-world settings is fought on multiple fronts. Novel neural interface and other hardware devices promise to improve the signal-to-noise rate of brain signals and user acceptance. Continued efforts in signal processing and artificial intelligence are enhancing the decoding capabilities of BCIs. New developments in the design principles of BCI systems, such as shared-control, hybrid BCI and co-adaptive user training are finding use in attempts to widen user access to BCI apparatuses. Additionally, increasing the user evaluation of established and novel BCI applications is broadening the scope of application and enriching the field with valuable end- and professional user feedback.

This Special Issue aims to collect papers on a broad spectrum of specific topics reflecting recent advances in the methodology, design and applicability of BCI. The following are indicative of the kind of topics under discussion:

Low-cost, portable, unobtrusive and robust sensors for brain–computer interfaces;
Open-source software platforms for BCI;
The combination of brain imaging technologies with physiological sensors
Brain–computer interface applications and user evaluation studies;
Novel signal processing and machine learning for BCI, with emphasis on transfer and deep learning methods;
New user training paradigms and advanced co-adaptive approaches for BCI learning;
Benchmarking studies and production of big datasets BCI methods.

Dr. Serafeim Perdikis
Dr. Athanasios Vourvopoulos
Guest Editors

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Published Papers (5 papers)

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Research

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17 pages, 2337 KiB

Open AccessArticle

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

by Madiha Rehman, Humaira Anwer, Helena Garay, Josep Alemany-Iturriaga, Isabel De la Torre Díez, Hafeez ur Rehman Siddiqui and Saleem Ullah

Sensors 2024, 24(21), 6965; https://doi.org/10.3390/s24216965 - 30 Oct 2024

Viewed by 708

Abstract

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models. Full article

(This article belongs to the Special Issue Advances in Principles, Methods and Applications of Brain-Computer Interaction)

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20 pages, 6023 KiB

Open AccessArticle

Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks

by Fatemeh Mohajelin, Sobhan Sheykhivand, Abbas Shabani, Morad Danishvar, Sebelan Danishvar and Lida Zare Lahijan

Sensors 2024, 24(18), 5883; https://doi.org/10.3390/s24185883 - 10 Sep 2024

Cited by 1 | Viewed by 1183

Abstract

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions—the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle. Full article

(This article belongs to the Special Issue Advances in Principles, Methods and Applications of Brain-Computer Interaction)

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16 pages, 1986 KiB

Open AccessArticle

Reducing Driving Risk Factors in Adolescents with Attention Deficit Hyperactivity Disorder (ADHD): Insights from EEG and Eye-Tracking Analysis

by Anat Keren, Orit Fisher, Anwar Hamde, Shlomit Tsafrir and Navah Z. Ratzon

Sensors 2024, 24(11), 3319; https://doi.org/10.3390/s24113319 - 23 May 2024

Cited by 1 | Viewed by 1783

Abstract

Adolescents with attention deficit hyperactivity disorder (ADHD) face significant driving challenges due to deficits in attention and executive functioning, elevating their road risks. Previous interventions targeting driving safety among this cohort have typically addressed isolated aspects (e.g., cognitive or behavioral factors) or relied on uniform solutions. However, these approaches often overlook this population’s diverse needs. This study introduces the “Drive-Fun” innovative intervention (DFI), aimed at enhancing driving skills among this vulnerable population. The intervention was tested in a pilot study including 30 adolescents aged 15–18, comparing three groups: DFI, an educational intervention, and a control group with no treatment. Assessments included a driving simulator, EEG, and Tobii Pro Glasses 2. Evaluation was conducted pre- and post-intervention and at a 3-month follow-up. Results indicated that the DFI group significantly improved in the simulated driving performance, attentional effort, and focused gaze time. The findings underscore that holistic strategies with personalized, comprehensive approaches for adolescents with ADHD are particularly effective in improving driving performance. These outcomes not only affirm the feasibility of the DFI but also highlight the critical role of sensor technologies in accurately measuring and enhancing simulator driving performance in adolescents with ADHD. Outcomes suggest a promising direction for future research and application. Full article

(This article belongs to the Special Issue Advances in Principles, Methods and Applications of Brain-Computer Interaction)

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13 pages, 2927 KiB

Open AccessArticle

In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface

by Seonghun Park, Minsu Kim, Hyerin Nam, Jinuk Kwon and Chang-Hwan Im

Sensors 2024, 24(2), 545; https://doi.org/10.3390/s24020545 - 15 Jan 2024

Viewed by 1571

Abstract

Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver’s attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the experiment, four visual stimuli were displayed on a laboratory-made head-up display (HUD). This allowed the participants to control the in-car environment by simply staring at a target visual stimulus, i.e., without pressing a button or averting their eyes from the front. The driving performances in two realistic driving tests—obstacle avoidance and car-following tests—were then compared between the manual control condition and SSVEP-BCI control condition using a driving simulator. In the obstacle avoidance driving test, where participants needed to stop the car when obstacles suddenly appeared, the participants showed significantly shorter response time (1.42 ± 0.26 s) in the SSVEP-BCI control condition than in the manual control condition (1.79 ± 0.27 s). No-response rate, defined as the ratio of obstacles that the participants did not react to, was also significantly lower in the SSVEP-BCI control condition (4.6 ± 14.7%) than in the manual control condition (20.5 ± 25.2%). In the car-following driving test, where the participants were instructed to follow a preceding car that runs at a sinusoidally changing speed, the participants showed significantly lower speed difference with the preceding car in the SSVEP-BCI control condition (15.65 ± 7.04 km/h) than in the manual control condition (19.54 ± 11.51 km/h). The in-car environment control system using SSVEP-based BCI showed a possibility that might contribute to safer driving by keeping the driver’s focus on the front and thereby enhancing the overall driving performance. Full article

(This article belongs to the Special Issue Advances in Principles, Methods and Applications of Brain-Computer Interaction)

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Review

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23 pages, 8837 KiB

Open AccessReview

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications

by Yu Diao, Yaoxuan Zhang, Yanran Li and Jie Jiang

Sensors 2023, 23(24), 9779; https://doi.org/10.3390/s23249779 - 12 Dec 2023

Cited by 1 | Viewed by 1921

Abstract

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems. Full article

(This article belongs to the Special Issue Advances in Principles, Methods and Applications of Brain-Computer Interaction)

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