Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …

Recently, convolutional neural networks (CNNs) have brought impressive improvements for
object detection. However, detecting targets in infrared images still remains challenging …

Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

The mental workload can be estimated by monitoring different mental states from neural
activity. The spectral power of EEG and Event-Related Potentials (ERPs) are the two …

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …

Currently, mental stress is a common social problem affecting people. Stress reduces
human functionality during routine work and may lead to severe health defects. Detecting …

Early-stage diagnosis of Brain Tumor leads to better chance of cure from this deadliest
disease across the globe. Existing schemes on brain tumor classification use machine …