Background Neuroergonomics combines neuroscience with ergonomics to study human
performance using recorded brain signals. Such neural signatures of performance can be …

The main hypothesis of this work is that the time of delay in reaction to an unexpected event
can be predicted on the basis of the brain activity recorded prior to that event. Such mental …

Hand movement (both physical and imaginary) is linked to the motor cortex region of human
brain. This paper aims to compare the left–right hand movement classification performance …

We propose a framework for building electrophysiological predictors of single-trial motor
performance variations, exemplified for SVIPT, a sequential isometric force control task …

Using neurophysiological measures to model how the brain performs complex cognitive
tasks such as mental rotation is a promising way towards precise predictions of behavioural …

The decoding of brain signals recorded via, eg, an electroencephalogram, using machine
learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other …

Abstract Current Impact Synchronous Modal Analysis (ISMA), an operational modal analysis
technique incorporated with either manual impact hammer or Automated Phase Controlled …

This paper is a compilation of the most recent machine learning methods used in the Berlin
Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has …

As oscillatory components of the Electroencephalogram (EEG) and other
electrophysiological signals may co-modulate in power with a target variable of interest (eg …

Parkinson's Disease is characterized by the loss of dopaminergic neurons in the substantia
nigra pars compacta (SNc). The SNc supplies the basal ganglia (BG) via dopaminergic …