Analyzing brain states that correspond to event related potentials (ERPs) on a single trial
basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio …

Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI
research was focused on applications for paralyzed patients, increasingly more alternative …

Rapid serial visual presentation (RSVP) combined with the detection of event-related brain
responses facilitates the selection of relevant information contained in a stream of images …

Objective: Feature extraction is one of the most important steps in any brain-computer
interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs …

Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's
electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) …

Parieto-occipital electroencephalogram (EEG) alpha power and subjective reports of
attentional state are both associated with visual attention and awareness, but little is …

Objective. Single-trial electroencephalography (EEG) classification is of great importance in
the rapid serial visual presentation (RSVP) task. Convolutional neural networks (CNNs), as …

A brain–computer interface (BCI) is an advanced human–machine interaction technology.
The BCI speller is a typical application that detects the stimulated source-induced EEG …