Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …

EEG is the recording of electrical activities of the brain, usually along the scalp surface,
which are the results of synaptic activations of the brain's neurons. In recent years, it has …

The performance of a classifier in a brain-computer interface (BCI) system is highly
dependent on the quality and quantity of training data. Typically, the training data are …

Prognostic and Health Management (PHM) systems are some of the main protagonists of
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …

Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …

Simple Summary This paper proposes a novel generative adversarial networks model,
SynSigGAN, to generate any kind of synthetic biomedical signals. The generation of …

Electroencephalography (EEG)-based affective computing has a scarcity problem. As a
result, it is difficult to build effective, highly accurate and stable models using machine …

The quality and quantity of training data are crucial to the performance of a deep-learning-
based brain-computer interface (BCI) system. However, it is not practical to record EEG data …

Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG)
based classification tasks is of significant interest for generalizability of decoding models …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …