Special Issue "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: 30 June 2021.

Special Issue Editors

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

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

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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 2000 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 (2 papers)

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Research

Open AccessArticle
On the Handwriting Tasks’ Analysis to Detect Fatigue
Appl. Sci. 2020, 10(21), 7630; https://doi.org/10.3390/app10217630 - 29 Oct 2020
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
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Open AccessArticle
A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications
Appl. Sci. 2020, 10(19), 6761; https://doi.org/10.3390/app10196761 - 27 Sep 2020
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
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