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

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

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