Enhanced EEG-based cognitive workload detection using RADWT and machine learning.

Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, brain-computer interfaces, and cognitive monitoring. In this work, different levels of cognitive workload are investigated, and a classification approach based on the Rational-Dilation Wavelet Transform (RADWT) is proposed.

Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neuro-developmental disorder of childhood. In this study we propose two classification algorithms for discriminating ADHD children from normal children using their resting state Electroencephalography (EEG) signals. One algorithm is based on the univariate features extracted from individual EEG recording channels and the other is based on the multivariate features extracted from brain lobes.

A novel multi-scale Hessian based spot enhancement filter for two dimensional gel electrophoresis images.

Two dimensional gel electrophoresis (2DGE) is a useful method for studying proteins in a wide variety of applications including identifying post-translation modification (PTM), biomarker discovery, and protein purification. Computerized segmentation and detection of the proteins are the two main processes that are carried out on the scanned image of the gel. Due to the complexities of 2DGE images and the presence of artifacts, the segmentation and detection of protein spots in these images are non-trivial, and involve supervised and time consuming processes.