  Electroencephalography (EEG) measures the electrical brain activity in
real-time by using sensors placed on the scalp. Artifacts, due to eye movements
and blink, muscular/cardiac activity and generic electrical disturbances, have
to be recognized and eliminated to allow a correct interpretation of the useful
brain signals (UBS) of EEG. Independent Component Analysis (ICA) is effective
to split the signal into independent components (ICs) whose re-projections on
2D scalp topographies (images), also called topoplots, allow to
recognize/separate artifacts and by UBS. Until now, IC topoplot analysis, a
gold standard in EEG, has been carried on visually by human experts and, hence,
not usable in automatic, fast-response EEG. We present a completely automatic
and effective framework for EEG artifact recognition by IC topoplots, based on
2D Convolutional Neural Networks (CNNs), capable to divide topoplots in 4
classes: 3 types of artifacts and UBS. The framework setup is described and
results are presented, discussed and compared with those obtained by other
competitive strategies. Experiments, carried on public EEG datasets, have shown
an overall accuracy of above 98%, employing 1.4 sec on a standard PC to
classify 32 topoplots, that is to drive an EEG system of 32 sensors. Though not
real-time, the proposed framework is efficient enough to be used in
fast-response EEG-based Brain-Computer Interfaces (BCI) and faster than other
automatic methods based on ICs.
