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Applied Sciences
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Recent Trends, Applications, and Challenges of Brain–Machine Interfaces
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Recent Trends, Applications, and Challenges of Brain–Machine Interfaces

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 10965

Special Issue Editors

Dr. Mufti Mahmud

E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
Interests: brain informatics; data analytics; brain–machine interfacing; Internet of Healthcare Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefano Vassanelli

E-Mail Website
Guest Editor
Department of Biomedical Sciences, University of Padua, Via U. Bassi 58/B, 35131 Padua, Italy
Interests: brain–machine interfacing; neuroengineering; neuron–chip interfacing
Dr. Gunasekaran Manogaran

E-Mail Website
Guest Editor
John Muir Institute of the Environment, University of California, Davis, CA 95616, USA
Interests: big data analytics; Internet of Things; health data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The brain–machine interface (BMI), also alternately referred to as the brain–computer interface (BCI), has emerged as an interdisciplinary field with practical applications to many disciplines, such as brain research, medical rehabilitation, neuroergonomics and smart environment, neuromarketing and advertisement, education and self-regulation, games and entertainment, and security and authentication. This involves a range of diverse data acquisition techniques recording brain signals from the scalp, subdural, subcortical, and deep brain structures. Divided into invasive and non-invasive categories, these signals include electrocorticograms (ECoG), intracortical signals such as local field potentials (LFP), and multi- and single-unit activities (neuronal spikes) for the invasive category, and electroencephalograms (EEG), magnetoencephalograms (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) for the non-invasive category. These signals require sophisticated processing before they can be used in the application area of BMI/BCI. There are numerous challenges in the pipeline from signal acquisition to application. This Special Issue thus aims to collate cutting-edge original research as well as comprehensive survey articles targeting recent trends, applications, and challenges of BMI/BCI.

Dr. Mufti Mahmud
Prof. Dr. Stefano Vassanelli
Dr. Gunasekaran Manogaran
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. Applied Sciences 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

  • Brain–machine interfacing in brain research
  • Brain–machine interfacing in medical rehabilitation
  • Brain–machine interfacing in neuroergonomics and smart environment
  • Brain–machine interfacing in Neuromarketing and advertisement
  • Brain–machine interfacing in education and self-regulation
  • Brain–machine interfacing in games and entertainment
  • Brain–machine interfacing in security and authentication
  • Signal acquisition challenges in brain–machine interfacing
  • Signal processing challenges in brain–machine interfacing

Published Papers (4 papers)

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Research

23 pages, 8173 KiB  
Article
Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures
by Irina E. Nicolae and Mihai Ivanovici
Appl. Sci. 2021, 11(9), 4306; https://doi.org/10.3390/app11094306 - 10 May 2021
Cited by 3 | Viewed by 2693
Abstract
In practical applications, such as patient brain signals monitoring, a non-invasive recording system with fewer channels for an easy setup and a wireless connection for remotely monitor physiological signals will be beneficial. In this paper, we investigate the feasibility of using such a [...] Read more.
In practical applications, such as patient brain signals monitoring, a non-invasive recording system with fewer channels for an easy setup and a wireless connection for remotely monitor physiological signals will be beneficial. In this paper, we investigate the feasibility of using such a system in a visual perception scenario. We investigate the complexity perception of color natural and synthetic fractal texture images, by studying the correlations between four types of data: image complexity that is expressed by computed color entropy and color fractal dimension, human subjective evaluation by scoring, and the measured brain EEG responses via Event-Related Potentials. We report on the considerable correlation experimentally observed between the recorded EEG signals and image complexity while considering three complexity levels, as well on the use of an EEG wireless system with few channels for practical applications, with the corresponding electrodes placement in accordance with the type of neural activity recorded. Full article
(This article belongs to the Special Issue Recent Trends, Applications, and Challenges of Brain–Machine Interfaces)
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18 pages, 3342 KiB  
Article
Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
by Yoshihiro Sato, Yuichiro Kitamura, Takamichi Hirata and Yue Bao
Appl. Sci. 2021, 11(3), 1045; https://doi.org/10.3390/app11031045 - 25 Jan 2021
Cited by 6 | Viewed by 1965
Abstract
This study focuses on the problem of eye irritation when measuring steady-state visual evoked potentials (SSVEPs) using a brain–computer interface and aims to clarify experimentally visual stimulus signals that do not cause discomfort to users. To this end, a method is proposed that [...] Read more.
This study focuses on the problem of eye irritation when measuring steady-state visual evoked potentials (SSVEPs) using a brain–computer interface and aims to clarify experimentally visual stimulus signals that do not cause discomfort to users. To this end, a method is proposed that introduces a flash stimulus in which the color is changed by changing its hue. This reduces the change in brightness while providing a color change, thereby facilitating visual stimulation with less discomfort. In experiments conducted, flash stimuli of the primary colors red, green, and blue and colors with different hues of 5–45° from these primary colors were generated to investigate the algorithm accuracy of SSVEP and discomfort. Subjective questionnaire and CFF values, which are ophthalmic parameters, were obtained for the subjects and compared to the discrimination rate. As a result of the comparison, it was confirmed that the fatigue level of the visual stimulus generated by the proposed hue change was lower than that of the conventional black-and-white stimulus. It was also confirmed that the combination of the hue difference and frequency could obtain the same discrimination rate as the conventional method. Full article
(This article belongs to the Special Issue Recent Trends, Applications, and Challenges of Brain–Machine Interfaces)
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16 pages, 1901 KiB  
Article
On the Handwriting Tasks’ Analysis to Detect Fatigue
by Manuel-Vicente Garnacho-Castaño, Marcos Faundez-Zanuy and Josep Lopez-Xarbau
Appl. Sci. 2020, 10(21), 7630; https://doi.org/10.3390/app10217630 - 29 Oct 2020
Cited by 4 | Viewed by 2136
Abstract
Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain–computer interface-operated systems), require good shape in both of [...] Read more.
Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain–computer interface-operated systems), require good shape in both of them. This paper presents a new online handwritten database of 20 healthy subjects. The main goal was to study the influence of several physical exercise stimuli in different handwritten tasks and to evaluate the recovery after strenuous exercise. To this aim, they performed different handwritten tasks before and after physical exercise as well as other measurements such as metabolic and mechanical fatigue assessment. Experimental results showed that although a fast mechanical recovery happens and can be measured by lactate concentrations and mechanical fatigue, this is not the case when cognitive effort is required. Handwriting analysis revealed that statistical differences exist on handwriting performance even after lactate concentration and mechanical assessment recovery. This points out a necessity of more recovering time in sport and professional activities than those measured in classic ways. Full article
(This article belongs to the Special Issue Recent Trends, Applications, and Challenges of Brain–Machine Interfaces)
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21 pages, 3531 KiB  
Article
A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications
by Ziyi Ju, Li Gun, Amir Hussain, Mufti Mahmud and Cosimo Ieracitano
Appl. Sci. 2020, 10(19), 6761; https://doi.org/10.3390/app10196761 - 27 Sep 2020
Cited by 5 | Viewed by 2564
Abstract
In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive [...] Read more.
In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%. Full article
(This article belongs to the Special Issue Recent Trends, Applications, and Challenges of Brain–Machine Interfaces)
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