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Bioengineering
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Advances in Artificial Intelligence and Machine Learning for BCI/BMI
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Advances in Artificial Intelligence and Machine Learning for BCI/BMI

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 21364

Special Issue Editors

Prof. Dr. Larbi Boubchir

E-Mail Website
Guest Editor
LIASD Research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: biomedical signal processing; EEG; image processing; machine learning; brain–computer interface; biometrics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuling Yan

E-Mail Website
Guest Editor
School of Engineering, Santa Clara University, Santa Clara, CA 95053, USA
Interests: biosignal processing; bioimaging; AI-assisted disease classification; laryngeal dynamics and physiology; biomedical visualization; brain-computer Interface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The brain–computer interface (BCI), also called the brain–machine interface (BMI), is an emerging technology involving software and hardware communication systems allowing the use of brain activity to control external devices such as computers, robots, and machines. BCI systems translate the activity of the brain to conduct an action or a command that will be executed by the external device. Artificial intelligence (AI)/machine learning (ML) has received great attention for the development of BCI applications to solve difficult problems in several domains, in particular, medical and robotic fields. AI/ML has since become the most efficient tool for BCI systems. This Special Issue aims to solicit original research papers as well as review articles focusing on recent advances in AI/ML for BCI research.

The main topics include, but are not limited to, the following:

  • Brain–computer interface (BCI)/Brain–machine interface (BMI)
  • Artificial intelligence in BCI/BMI
  • Machine learning in BCI/BMI
  • Deep learning in BCI/BMI
  • Brain signal processing for BCI/BMI
  • Neurofeedback
  • Neural Rehabilitation Engineering
  • Related applications

Assoc. Prof. Dr. Larbi Boubchir
Prof. Dr. Yuling Yan
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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.

Published Papers (3 papers)

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Research

14 pages, 685 KiB  
Article
A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting
by Andrea Valenti, Michele Barsotti, Davide Bacciu and Luca Ascari
Bioengineering 2021, 8(2), 21; https://doi.org/10.3390/bioengineering8020021 - 05 Feb 2021
Cited by 8 | Viewed by 3382
Abstract
Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this [...] Read more.
Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects’ movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning for BCI/BMI)
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14 pages, 3989 KiB  
Article
Current Practice in Preoperative Virtual and Physical Simulation in Neurosurgery
by Elisa Mussi, Federico Mussa, Chiara Santarelli, Mirko Scagnet, Francesca Uccheddu, Rocco Furferi, Yary Volpe and Lorenzo Genitori
Bioengineering 2020, 7(1), 7; https://doi.org/10.3390/bioengineering7010007 - 03 Jan 2020
Cited by 21 | Viewed by 7501
Abstract
In brain tumor surgery, an appropriate and careful surgical planning process is crucial for surgeons and can determine the success or failure of the surgery. A deep comprehension of spatial relationships between tumor borders and surrounding healthy tissues enables accurate surgical planning that [...] Read more.
In brain tumor surgery, an appropriate and careful surgical planning process is crucial for surgeons and can determine the success or failure of the surgery. A deep comprehension of spatial relationships between tumor borders and surrounding healthy tissues enables accurate surgical planning that leads to the identification of the optimal and patient-specific surgical strategy. A physical replica of the region of interest is a valuable aid for preoperative planning and simulation, allowing the physician to directly handle the patient’s anatomy and easily study the volumes involved in the surgery. In the literature, different anatomical models, produced with 3D technologies, are reported and several methodologies were proposed. Many of them share the idea that the employment of 3D printing technologies to produce anatomical models can be introduced into standard clinical practice since 3D printing is now considered to be a mature technology. Therefore, the main aim of the paper is to take into account the literature best practices and to describe the current workflow and methodology used to standardize the pre-operative virtual and physical simulation in neurosurgery. The main aim is also to introduce these practices and standards to neurosurgeons and clinical engineers interested in learning and implementing cost-effective in-house preoperative surgical planning processes. To assess the validity of the proposed scheme, four clinical cases of preoperative planning of brain cancer surgery are reported and discussed. Our preliminary results showed that the proposed methodology can be applied effectively in the neurosurgical clinical practice both in terms of affordability and in terms of simulation realism and efficacy. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning for BCI/BMI)
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18 pages, 4582 KiB  
Article
Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)
by Yar Muhammad and Daniil Vaino
Bioengineering 2019, 6(2), 46; https://doi.org/10.3390/bioengineering6020046 - 17 May 2019
Cited by 8 | Viewed by 8496
Abstract
The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and [...] Read more.
The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence and Machine Learning for BCI/BMI)
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