Assessment of the cognitive functions and state of clinical subjects is an important aspect of
e-health care delivery, and in the development of novel human-machine interfaces. A …

Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …

Owing to the proximity of the ear canal to the central nervous system, in-ear
electrophysiological systems can be used to unobtrusively monitor brain states. Here, by …

Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …

One of the greatest challenges that hinder the decoding and application of
electroencephalography (EEG) is that EEG recordings almost always contain artifacts-non …

This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …

Objective. Deep learning (DL) networks are increasingly attracting attention across various
fields, including electroencephalography (EEG) signal processing. These models provide …

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by
enhancing the trust of end-users in machines. As the number of connected devices keeps on …

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide
high quality neuroscientific data for open access to the scientific community. As experienced …

Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …