  Brain-computer interfaces (BCIs) have been gaining momentum in making
human-computer interaction more natural, especially for people with
neuro-muscular disabilities. Among the existing solutions the systems relying
on electroencephalograms (EEG) occupy the most prominent place due to their
non-invasiveness. However, the process of translating EEG signals into computer
commands is far from trivial, since it requires the optimization of many
different parameters that need to be tuned jointly. In this report, we focus on
the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked
Potentials (SSVEPs) and perform a comparative evaluation of the most promising
algorithms existing in the literature. More specifically, we define a set of
algorithms for each of the various different parameters composing a BCI system
(i.e. filtering, artifact removal, feature extraction, feature selection and
classification) and study each parameter independently by keeping all other
parameters fixed. The results obtained from this evaluation process are
provided together with a dataset consisting of the 256-channel, EEG signals of
11 subjects, as well as a processing toolbox for reproducing the results and
supporting further experimentation. In this way, we manage to make available
for the community a state-of-the-art baseline for SSVEP-based BCIs that can be
used as a basis for introducing novel methods and approaches.
