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Computational Intelligence Based-Brain-Body Machine Interface
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Computational Intelligence Based-Brain-Body Machine Interface

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 4955

Special Issue Editor

Dr. Rifai Chai

E-Mail Website
Guest Editor
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: brain-computer interface; biomedical signal processing; biomedical instrumentation; computational intelligence and machine learning

Special Issue Information

Dear Colleagues,

The research focused on brain–body–machine interfaces and their computational intelligence. Brain–body–machine interfacing is a rapidly expanding field of research, and it provides the link between a human and an external machine by using different types of modalities such as electroencephalogram/EEG (brain signal), magnetoencephalogram/MEG (brain data/image), functional magnetic resonance imaging/fMRI (brain image), functional near-infrared spectroscopy/fNIRS (brain image/data), electrocorticogram/ECoG (brain signal), local field potential/LFP (brain signal), electromyogram/EMG (muscle signal), electrocardiogram/ECG, electrooculogram/EOG, inertia measurement unit/IMU (body movement) and camera (body movement).

The components for the computational intelligence of the brain–body–machine interface consist of several elements, including signal acquisition, signal pre-processing, features extraction and classification or translation modules, which will give the output of commands or logical control signals to operate application devices that replace, restore, enhance and supplement the natural way that the central nervous system functions.

This Special Issue will explore original research on recent advances, technologies, solutions, computational intelligence, applications and new challenges in brain–body–machine–computer interfaces.

Possible topics include, but are not limited to:

  • Brain–computer interface (BCI)/brain–machine interface (BMI) and applications with different modalities, EEG, MEG, fMRI, fNIRS, ECoG, Spikes, LFP and microelectrodes.
  • Body–machine interfaces using EMG, ECG, EOG, IMU and camera.
  • Hybrid/multimodal human–machine interface.
  • Multimodal feature extraction and classification for brain–body–machine interfaces.
  • Computational intelligence for brain–body–machine interface pattern recognition.
  • Brain and body signal processing.
  • Machine learning and deep learning used in brain–body–machine interfaces.
  • Online and offline brain–body–machine interfaces.
  • Novel sensor technologies for brain and body machine interfaces.

Dr. Rifai Chai
Guest Editor

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. Sensors 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 2600 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 interface (BMI)
  • brain–computer interface (BCI)
  • body–machine interface
  • electroencephalogram (EEG)
  • magnetoencephalogram (MEG)
  • functional magnetic resonance imaging (fMRI)
  • functional near-infrared spectroscopy (fNIRS)
  • electrocorticogram (ECoG)
  • local field potential (LFP)
  • electromyogram (EMG)
  • electrocardiogram (ECG)
  • electrooculogram (EOG)
  • inertia measurement unit (IMU)
  • computational intelligence

Published Papers (3 papers)

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Research

18 pages, 5012 KiB  
Article
Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition
by Meirista Wulandari, Rifai Chai, Basari Basari and Dadang Gunawan
Sensors 2024, 24(2), 341; https://doi.org/10.3390/s24020341 - 6 Jan 2024
Viewed by 1152
Abstract
Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered [...] Read more.
Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER. Full article
(This article belongs to the Special Issue Computational Intelligence Based-Brain-Body Machine Interface)
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11 pages, 1051 KiB  
Communication
One Definition to Join Them All: The N-Spherical Solution for the EEG Lead Field
by Ricardo Bruña, Giorgio Fuggetta and Ernesto Pereda
Sensors 2023, 23(19), 8136; https://doi.org/10.3390/s23198136 - 28 Sep 2023
Viewed by 724
Abstract
Albeit its simplicity, the concentric spheres head model is widely used in EEG. The reason behind this is its simple mathematical definition, which allows for the calculation of lead fields with negligible computational cost, for example, for iterative approaches. Nevertheless, the literature shows [...] Read more.
Albeit its simplicity, the concentric spheres head model is widely used in EEG. The reason behind this is its simple mathematical definition, which allows for the calculation of lead fields with negligible computational cost, for example, for iterative approaches. Nevertheless, the literature shows contradictory formulations for the electrical solution of this head model. In this work, we study several different definitions for the electrical lead field of a four concentric spheres conduction model, finding that their results are contradictory. A thorough exploration of the mathematics used to build these formulations, provided in the original works, allowed for the identification of errors in some of the formulae, which proved to be the reason for the discrepancies. Moreover, this mathematical review revealed the iterative nature of some of these formulations, which allowed us to develop a formulation to solve the lead field in a head model built from an arbitrary number of concentric, homogeneous, and isotropic spheres. Full article
(This article belongs to the Special Issue Computational Intelligence Based-Brain-Body Machine Interface)
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20 pages, 5141 KiB  
Article
Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI
by Liuyin Yang and Marc M. Van Hulle
Sensors 2023, 23(3), 1704; https://doi.org/10.3390/s23031704 - 3 Feb 2023
Cited by 3 | Viewed by 2559
Abstract
Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, [...] Read more.
Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair—the latter raises safety concerns—thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes’ lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control. Full article
(This article belongs to the Special Issue Computational Intelligence Based-Brain-Body Machine Interface)
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