Recognition of epileptic seizure type is essential for the neurosurgeon to understand the
cortical connectivity of the brain. Though automated early recognition of seizures from …

Mental workload assessment is essential for maintaining human health and preventing
accidents. Most research on this issue is limited to a single task. However, cross-task …

In this article, the fixed-time (FXT) synchronization of discontinuous competitive neural
networks (CNNs) involving time-varying delays is investigated. Firstly, two kinds of …

Due to the emergence of deep learning, signal recognition has made great strides in
performance improvement. The success of most deep learning methods relies on the …

Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern
recognition, we report on the development and evaluation of brain signal classifiers based …

Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern
recognition, we report on the development and evaluation of brain signal classifiers based …

This work presents a novel approach for automatic epilepsy seizure detection based on EEG
analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main …

This paper presents theoretical results on multiple asymptotical ω-periodicity of a state-
dependent switching fractional-order neural network with time delays and sigmoidal …

Food safety is vital to the national economy and livelihood of people. Therefore, effective
food safety warnings are helpful to the healthy and sustainable development of society …

The scalp electroencephalogram (EEG) has been extensively studied for epileptic signal
classification in the past, but little attention has been paid to the data imbalance among …