Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.

The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications: functioning of brain-computer interface (BCI), detection of neuronal disorders, neurorehabilitation for disabled patients, and many others. Recent studies show that the combination or fusion of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) demonstrates improved classification and detection performance compared to sole-EEG and sole-fNIRS. Deep learning (DL) networks are suitable for the classification of large volume time-series data like EEG and fNIRS.

Retrospective Study of Diabetic Retinopathy and Macular Edema in Southern Part of Bangladesh.

Background: Diabetic mellitus is a vision-threatening disease because it causes diabetic retinopathy worldwide. The main focus of this research is to determine the prevalence and assess the visual outcome in diabetic retinopathy and macular edema patients by injecting Bevacizumab clinically.

Methods: This hospital-based trial case was conducted in Khulna BNSB Eye Hospital, Bangladesh.