Electroencephalography (EEG) has countless applications across many of fields. However,
EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts …

Nowadays, the interest in the Brain-Computer Interfacing (BCI) domain is continuously
growing, only judging by the number of BCI related papers published or presented in neuro …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

A key strategy to enable training of deep neural networks is to use non-saturating activation
functions to reduce the vanishing gradient problem. Popular choices that saturate only in the …

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices
raises the problem of the quality of the data recorded in such uncontrolled environments …

Ocular artifact, namely eye-blink artifact, is an inevitable and one of the most destructive
noises of EEG signals. Many solutions of detecting the eye-blink artifact were proposed …

The role of machine learning in neuroscience has been increasing through the years, in
aiding diagnosis, biomarker discovery, signal analysis, and other applications. However, the …

The widespread adoption of smart home technologies has resulted in the generation of vast
amounts of data related to home appliance usage. This research aims to harness the power …

Hidden and unexpected value can be found in the vast amounts of data generated by IoT
devices and industrial sensors. Extracting this knowledge can help on more complex tasks …

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in
the brain. These seizures manifest as various symptoms including muscle contractions and …