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Electronics
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New Advances of Brain-Computer and Human-Robot Interaction
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New Advances of Brain-Computer and Human-Robot Interaction

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 5662

Special Issue Editors

Prof. Dr. Alejandro L. Borja

E-Mail Website
Guest Editor
Departamento de Ingeniería Eléctrica, Electrónica, Automática y Comunicaciones, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: neurocognitive deficits; brain stimulation; electroencephalography; brain computer interface; machine learning; deep learning; neural networks; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Sanchez-Reolid

E-Mail Website
Guest Editor
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: emotional analysis; brain stimulation; electroencephalography; brain computer interface; machine learning; deep learning; neural networks; signal processing; electrodermal activity

Special Issue Information

Dear Colleagues,

In recent years, remarkable strides have been made in the fields of brain–computer interaction (BCI) and human–robot interaction (HRI), ushering in an era of unprecedented connectivity between humans and technology. BCI has undergone a revolution, opening exciting possibilities for individuals with disabilities and expanding our understanding of the human brain. Brain–computer interfaces allow for direct communication between the human brain and external devices, offering new avenues to enable those with paralysis or neurodegenerative diseases to regain control over their environment. With applications in neurorehabilitation, communication, and entertainment, BCI has unveiled the potential to enhance human capabilities beyond traditional means. Neuroscientists and engineers are constantly refining technology, making it more accessible and user-friendly, which promises to shape the way we interact with the digital world.

Simultaneously, HRIs have evolved to create more intuitive and natural interactions between humans and robots, blurring the lines between man and machine. With the advent of advanced sensors, machine learning algorithms, and artificial intelligence, robots can now perceive and respond to human emotions, gestures, and speech with greater accuracy and empathy. These advancements have engendered the use of HRI in various domains: from healthcare to manufacturing; in making robots—not mere tools, but companions and caregivers; and enhancing our quality of life.

These new advances in BCI and HRI mark a significant leap forward in our relationship with technology, with profound implications for society. As the boundaries between humans and machines continue to blur, it is essential to navigate the ethical and social implications of these technologies while embracing the opportunities they offer for a brighter, more inclusive, and interconnected future.

This Special Issue invites original research papers that report on recent advancements in BCI and HRI across a wide range of investigations and their practical applications. Prospective authors are invited to submit high-quality contributions and reviews.

Dr. Alejandro L. Borja
Dr. Roberto Sanchez-Reolid
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. Electronics 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 2400 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

  • signal processing
  • electroencephalography
  • brain–computer interface
  • neural signal processing techniques (EEG, MEG, MRI/fMRI, PET, fNIRS)
  • machine learning
  • deep learning
  • neural networks
  • neuroscience

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Published Papers (5 papers)

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Research

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39 pages, 13137 KiB  
Article
Neural Network-Based Emotion Classification in Medical Robotics: Anticipating Enhanced Human–Robot Interaction in Healthcare
by Waqar Riaz, Jiancheng (Charles) Ji, Khalid Zaman and Gan Zengkang
Electronics 2025, 14(7), 1320; https://doi.org/10.3390/electronics14071320 - 27 Mar 2025
Viewed by 334
Abstract
This study advances artificial intelligence by pioneering the classification of human emotions (for patients) with a healthcare mobile robot, anticipating human–robot interaction for humans (patients) admitted in hospitals or any healthcare environment. This study delves into the challenge of accurately classifying humans emotion [...] Read more.
This study advances artificial intelligence by pioneering the classification of human emotions (for patients) with a healthcare mobile robot, anticipating human–robot interaction for humans (patients) admitted in hospitals or any healthcare environment. This study delves into the challenge of accurately classifying humans emotion as a patient emotion, which is a critical factor in understanding patients’ recent moods and situations. We integrate convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multi-layer perceptrons (MLPs) to analyze facial emotions comprehensively. The process begins by deploying a faster region-based convolutional neural network (Faster R-CNN) to swiftly and accurately identify human emotions in real-time and recorded video feeds. This includes advanced feature extraction across three CNN models and innovative fusion techniques, which strengthen the improved Inception-V3 for superior accuracy and replace the improved Faster R-CNN feature learning module. This valuable replacement aims to enhance the accuracy of face detection in our proposed framework. Carefully acquired these datasets in a simulated environment. Validation on the EMOTIC, CK+, FER-2013, and AffectNet datasets all showed impressive accuracy rates of 98.01%, 99.53%, 99.27%, and 96.81%, respectively. These class-wise accuracy rates show that it has the potential to advance the medical environment and measures in the intelligent manufacturing of healthcare mobile robots. Full article
(This article belongs to the Special Issue New Advances of Brain-Computer and Human-Robot Interaction)
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24 pages, 5790 KiB  
Article
A Novel Multi-Feedback Differential Filter Instrumentation Amplifier for Βiosignals Acquisition Applications
by Athanasios Delis, Despoina-Polyxeni Georgiou, Ioannis Stamelos, Eleni Alexandratou and Konstantinos Politopoulos
Electronics 2025, 14(1), 95; https://doi.org/10.3390/electronics14010095 - 29 Dec 2024
Viewed by 956
Abstract
Efficient filtering in biosignals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown; thus, using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, [...] Read more.
Efficient filtering in biosignals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown; thus, using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, main’s interference, and the artifacts. In this context, the design and utilization of filters in the analog front end of biosensors, in conjunction with the first amplification stage, is not common but offers substantial advantages. In this study, the design of a novel Multi-feedback Differential Filter Instrumentation Amplifier (MFDFIA) is proposed. The design and the equations governing the gain and bandwidth characteristics of the MFDFIA are presented, and relevant topologies are explored. Even though MFDFIA has two op-amps in its first stage, due to its symmetric topology, the analysis can be conducted separately for the differential- and common-mode input signal with a simplified one op-amp equivalent circuit. Notably, MFDFIA’s CMRR is equal and depends only on the CMRR of the second stage. An exemplary simulation for EEG signal acquisition is provided, with a flat band of 1db between 0.7 Hz and 25.4 Hz, a gain of 34.1 db, and an input noise of 70.66 nVrms in the range of 0.1–10 Hz. Full article
(This article belongs to the Special Issue New Advances of Brain-Computer and Human-Robot Interaction)
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19 pages, 633 KiB  
Article
EEG and fNIRS Signal-Based Emotion Identification by Means of Machine Learning Algorithms During Visual Stimuli Exposure
by Daniel Sánchez-Reolid, Eloy García-Pérez, Alejandro L. Borja, Antonio Fernández-Caballero and Roberto Sánchez-Reolid
Electronics 2024, 13(23), 4797; https://doi.org/10.3390/electronics13234797 - 5 Dec 2024
Viewed by 1565
Abstract
This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such [...] Read more.
This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such as happiness, sadness, fear, and anger. Brain activity was recorded using the Emotiv EPOC X and NIRSport2 devices separately. The recorded signals were then processed and analyzed to identify the primary brain regions activated during the trials. Next, machine learning methods were employed to classify the evoked emotions with highest accuracy values of 71.3% for EEG data with a Multi-Layer Perceptron (MLP) method and 64.0% for fNIRS data using a Bagging Trees (BAG) algorithm. This approach not only highlights the effectiveness of using EEG and fNIRS technologies but also provides insights into the complex interplay between different brain areas during emotional experiences. By leveraging these advanced acquisition techniques, this study aims to contribute to the broader field of affective neuroscience and improve the accuracy of emotion recognition systems. The findings could have significant implications for developing intelligent systems capable of more empathetic interactions with humans, enhancing applications in areas such as mental health, human–computer interactions, or adaptive learning environments, among others. Full article
(This article belongs to the Special Issue New Advances of Brain-Computer and Human-Robot Interaction)
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19 pages, 5748 KiB  
Article
Research on an Indoor Light Environment Comfort Evaluation Index Based on Electroencephalogram and Pupil Signals
by Peiyuan Tian, Guanghua Xu, Chengcheng Han, Xiaowei Zheng, Kai Zhang, Chenghang Du, Xun Zhang, Fan Wei, Yunhao Ma, Sicong Zhang and Qingqiang Wu
Electronics 2024, 13(17), 3411; https://doi.org/10.3390/electronics13173411 - 27 Aug 2024
Viewed by 962
Abstract
With the development of modern technology, many people work for a long time around various artificial light sources and electronic equipment, causing them to feel discomfort in their eyes and even eye diseases. The industry currently lacks an objective quantitative environmental–visual comfort index [...] Read more.
With the development of modern technology, many people work for a long time around various artificial light sources and electronic equipment, causing them to feel discomfort in their eyes and even eye diseases. The industry currently lacks an objective quantitative environmental–visual comfort index that combines subjective and objective indicators. For this experiment, objective eye movement and electroencephalogram (EEG) signals were collected in combination with a subjective questionnaire survey and a preference inquiry for comprehensive data mining. Finally, the results on a Likert scale show that high screen brightness can reduce the visual fatigue of subjects under high illuminance and high correlated color temperature (CCT). Pupil data show that, under medium and high ambient illuminance, visual perception sensitivity is more likely to be stimulated, and visual fatigue is more likely to deepen. EEG data show that visual fatigue is related to illuminance and screen brightness. On this basis, this study proposes a new evaluation index, the visual comfort level (0.6404 average at a low screen brightness, 0.4218 average at a medium screen brightness, and 0.5139 average at a high screen brightness), where a higher score for the visual comfort level represents a better visual experience. The visual comfort level provides a useful reference for enhancing the processing of multi-dimensional and biomedical signals and protecting the eyes. Full article
(This article belongs to the Special Issue New Advances of Brain-Computer and Human-Robot Interaction)
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34 pages, 1705 KiB  
Systematic Review
Challenges and Opportunities of Gamified BCI and BMI on Disabled People Learning: A Systematic Review
by Bilal Ahmed, Sumbal Khan, Hyunmi Lim and Jeonghun Ku
Electronics 2025, 14(3), 491; https://doi.org/10.3390/electronics14030491 - 25 Jan 2025
Cited by 1 | Viewed by 972
Abstract
This systematic review explores the potential of the gamified brain–machine interfaces (BMIs) and brain–computer interfaces (BCIs) to enhance the quality of life for individuals with disabilities. These technologies promise to solve complex problems by delivering customized interventions considering individual needs, ethical dilemmas, and [...] Read more.
This systematic review explores the potential of the gamified brain–machine interfaces (BMIs) and brain–computer interfaces (BCIs) to enhance the quality of life for individuals with disabilities. These technologies promise to solve complex problems by delivering customized interventions considering individual needs, ethical dilemmas, and practical constraints. This review follows the PRISMA statement. The search process extensively explored multiple registered databases for studies published between 2015 and 2024. Articles were selected based on strict eligibility criteria, focusing on empirical research evaluating gamified BCIs and BMIs in rehabilitation and learning. The final analysis included 56 studies. A thorough examination emphasizes the transformative potential of gamified BCIs and BMIs for people with disabilities, highlighting the need for interdisciplinary collaboration, user-centered design principles, and ethical consciousness for gamified neurotechnology. These technologies mark a significant change by providing enjoyable and effective treatments for disabled individuals. It also delves into how gamification, neurofeedback, and adaptive learning techniques can enhance motivation, engagement, and overall well-being. This evaluation underscores the efficiency of gamified BCIs and BMIs as potential instruments for improving the quality of life and empowering disabled people. However, despite their apparent potential for rehabilitation and learning, more research is needed to validate their effectiveness, accessibility, and long-term benefits. Full article
(This article belongs to the Special Issue New Advances of Brain-Computer and Human-Robot Interaction)
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