The development of seizure prediction models is often based on long-term scalp
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …

Epilepsy is a neurological condition characterized by temporary disruptions in the brain's
electrical activity. This disorder can significantly impact the quality of life for those affected …

With the emergence of 5G technology, congestion control become a vital and main
challenge to be addressed in order to have efficient communication. There are several …

The goal of this paper is to use a brain-computer interface (BCI) and artificial intelligence for
EEG signal analysis. This includes answering the question whether it is possible to create a …

Electroencephalogram (EEG) are the neuro-electrophysiology signals, which are commonly
used as a diagnostic tool to measure the seizure activity of the brain. The accurate detection …

Data stream mining techniques have recently received increasing research interest,
especially in medical data classification. An unbalanced representation of the classification's …

The four-volume set LNICST 532, 533, 534 and 535 constitutes the refereed proceedings of
the 5th EAI International Conference on Multimedia Technology and Enhanced Learning …

It's important to note that the classification of epileptic seizures can be complex, and an
individual may experience more than one type of  seizure. Additionally, advancements in …

Data about time series has been researched for ages in various fields. In past few years,
with the advancements of the Internet of Things (IoT) and the use of data acquisition devices …

This paper presents FAS-CT a novel approach to a distributed low-latency Deep Learning
inference system based on a Field Programmable Gate Array (FPGA). The system …