Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …

Objective. The electroencephalogram (EEG) is an essential neuro-monitoring tool for both
clinical and research purposes, but is susceptible to a wide variety of undesired artifacts …

This paper considers the auditory attention detection (AAD) paradigm, where the goal is to
determine which of two simultaneous speakers a person is attending to. The paradigm relies …

Objective Electroencephalographic (EEG) data are often contaminated with non-neural
artifacts which can confound experimental results. Current artifact cleaning approaches …

Unlike traditional homogeneous single-task wireless sensor networks (WSNs),
heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous …

Objective Electroencephalography (EEG) is often used to examine neural activity time-
locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG …

Objective: We aim to extract and denoise the attended speaker in a noisy two-speaker
acoustic scenario, relying on microphone array recordings from a binaural hearing aid …

Many real-life signals are compressible, meaning that they depend on much fewer
parameters than their sample size. In this paper, we use low-rank matrix or tensor …

Objective. To develop an efficient, embedded electroencephalogram (EEG) channel
selection approach for deep neural networks, allowing us to match the channel selection to …

Objective: the recent emergence and success of electroencephalography (EEG) in low-cost
portable devices, has opened the door to a new generation of applications processing a …