Machine Learning and Its Application in Neuroscience and Brain–Computer Interfaces

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 424

Special Issue Editors


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Guest Editor
Biomedical Engineering, University of California, Davis, CA 95616, USA
Interests: Python (programming language); machine learning; computer vision; theory of computation and compilers

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Guest Editor

Special Issue Information

Dear Colleagues,

Recent technological innovations have led to substantial disruptions in fields like medicine and healthcare, patient monitoring, and telemedicine. Brain–computer interfaces are a novel and highly significant technology that establishes an information-sharing pathway between an external device such as a computer and the electrical signals in the brain. With this technology, it is possible for even a paralyzed patient to communicate their thoughts to a computer. The technology is made possible through three common methods that define how the electrodes make contact with the brain tissues. These methods are invasive (microelectrode array), partially invasive (endovascular and ECoG), and non-invasive (MRI, EEG, EOG, MEG).

The most common function in a brain–computer interface is the extraction of relevant features and the decoding of brain signals from the recorded datasets. Machine learning and deep learning are becoming highly important, particularly in the processing of such large datasets, due to their computational power. Although a great deal of research and theories have been formed around this technology, there is much room for development. For instance, machine learning research can focus on overcoming the noise and instability associated with brain signals. However, there is a great deal of developmental-stage research that is being conducted with the help of machine learning and neural networks. To illustrate the application potential of such technologies in neuroscience and brain–computer interfaces, let us consider a dataset with n number of data points plotted against a graph. Consider also that two types of brain waves overlap each other at various points in the graph. In order to represent these brain waves as a curve in space, we need to fit it into n dimensional spaces to obtain n dimensional curves. Traditional methods cannot handle the complexity associated with so many dimensions. Machine learning approaches can be leveraged to represent the whole brainwave as a single point. By doing so, brainwaves that represent the desired responses can be easily detected or captured. For instance, anticipation or surprise responses can be easily captured in this way from EEG signals. The most common types of machine learning algorithms that are currently in use include support vector machine (SVM), neural networks, and supervised learning. This Special Issue invites researchers working in this field to submit their novel and innovative contributions that fall within the scope of Machine Learning and Its Application in Neuroscience and Brain–Computer Interfaces.

Topics of interest for this SI include, but are not limited to, the following:

  • Convolutional neural networks (CNNs) for neuroimaging applications.
  • Development of a brain–computer interfaces by integrating machine learning approaches with Riemannian geometry.
  • Methods to enhance motor imagery classification using machine learning for brain–computer interfaces.
  • Novel deep learning techniques for the identification of optical simulation points through electrophysiological response.
  • Techniques to ensure privacy and security in brain–computer interfaces.
  • Neurological rehabilitation and plasticity augmentation using ML for brain–computer interfaces.
  • Design of a highly accurate multimodal brain–computer interface.
  • Design and development of a brain–computer interface for real-time control of neuroprosthetic hands using machine learning.
  • Brain–computer interface for initiating alarm during fight and flight responses.
  • Development of a brain-actuated wheelchair using a brain–computer interface and machine learning.

Dr. Dinesh Jackson Samuel
Prof. Dr. Seifedine Kadry
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. Brain Sciences 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 2200 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

There is no accepted submissions to this special issue at this moment.
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