Electroencephalogram (EEG), boasting the advantages of portability, low cost, and
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …

Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …

Background and objective In recent years, several automatic sleep stage classification
methods based on convolutional neural networks (CNN) by learning hierarchical feature …

Electroencephalography (EEG) is the signal of intrigue that has immense application in the
clinical diagnosis of various neurological, psychiatric, psychological, psychophysiological …

Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the
safety of road driving. Past statistical data on road accidents has shown enormous increases …

Background and objective Despite numerous deep learning methods being developed for
automatic sleep stage classification, almost all the models need labeled data. However …

There are many sources of interference encountered in the electroencephalogram (EEG)
recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is …

Since the medical professionals require to segment the electroencephalograms (EEGs) into
the pieces, study each piece of the EEGs and perform the manual annotation on these …

The EEG signals (electroencephalogram) are one of the most used biosignals in diagnosis
of neurological disorders and technology like brain computer interface (BCI). The main …

Multimodal signal analysis based on sophisticated sensors, efficient communication systems
and fast parallel processing methods has a rapidly increasing range of multidisciplinary …