Brain computer interface technology represents a highly growing field of research with
application systems. Its contributions in medical fields range from prevention to neuronal …

Controlling a device with a brain-computer interface requires extraction of relevant and
robust features from high-dimensional electroencephalographic recordings. Spatial filtering …

Classifying motion intentions in brain–computer interfacing (BCI) is a demanding task as the
recorded EEG signal is not only noisy and has limited spatial resolution but it is also …

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …

Because of the increasing portability and wearability of noninvasive electrophysiological
systems that record and process electrical signals from the human brain, automated systems …

In this paper, a general overview regarding neural recording, classical signal processing
techniques and machine learning classification algorithms applied to monitor brain activity is …

Objective. Electroencephalogram (EEG) recordings are often contaminated with artifacts.
Various methods have been developed to eliminate or weaken the influence of artifacts …

An approach to the direct measurement of perception of video quality change using
electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their …

A major challenge in electroencephalogram (EEG)-based brain-computer interfaces (BCIs)
is the inherent nonstationarities in the EEG data. Variations of the signal properties from intra …

Channel selection is a critical part of the classification procedure for multichannel
electroencephalogram (EEG)-based brain–computer interfaces (BCI). An optimized subset …