Electroencephalogram (EEG), boasting the advantages of portability, low cost, and
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …

Standard artifact removal methods for electroencephalographic (EEG) signals are either
based on Independent Component Analysis (ICA) or they regress out ocular activity …

Brain-computer interfaces are sophisticated signal processing systems, which directly
operate on neuronal signals to identify specific human intents. These systems can be …

Recent advances in 3D displays have contributed to the pressing need of new measurement
methods for display comfort. Develo** a valid measurement of visual fatigue caused by …

The most common approach to reduce muscle artifacts in electroencephalographic signals
is to linearly decompose the signals in order to separate artifactual from neural sources …

Independent component analysis (ICA) as a promising spatial filtering method can separate
motor-related independent components (MRICs) from the multichannel …

Goal: Current brain-computer interfaces (BCIs) are usually based on various, often
supervised, signal processing methods. The disadvantage of supervised methods is the …

It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of
a brain‐computer interface (BCI) and diagnosis of brain diseases in clinical research …

A current trend in the development of assistive devices for rehabilitation, for example
exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality …

Abstract The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for
electroencephalographic (EEG) data, as made freely available in the open-source EP …