Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for
the measurement of electrical brain activity which is extensively used in some areas of …

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose
significant threats to human mortality, morbidity, and functional independence. Brain …

Abstract Brain–computer interfaces (BCIs), representing a transformative form of human–
computer interaction, empower users to interact directly with external environments through …

Most of the existing object detection algorithms are trained on medical datasets and then
used for prediction. When the features of an object are not obvious in an image, these …

Background Spinal cord injury (SCI) may lead to impaired motor function, autonomic
nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system …

Accurately characterizing brain activity requires detailed feature analysis in the temporal,
spatial, and spectral domains. While previous research has proposed various spatial and …

Brain–computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to
interact with the world through brain signals. To meet demands of real-time, stable, and …

Pain is an experience of unpleasant sensations and emotions associated with actual or
potential tissue damage. In the global context, billions of people are affected by pain …

To enhance deep learning-based automated interictal epileptiform discharge (IED)
detection, this study proposes a multimodal method, vEpiNet, that leverages video and …

Dimensionality reduction is a critical factor in processing high-dimensional datasets. The L1
norm-based Two-Dimensional Linear Discriminant Analysis (L1-2DLDA) is widely used for …