  Objective: To evaluate the impact on Electroencephalography (EEG)
classification of different kinds of attention mechanisms in Deep Learning (DL)
models. Methods: We compared three attention-enhanced DL models, the brand-new
InstaGATs, an LSTM with attention and a CNN with attention. We used these
models to classify normal and abnormal (i.e., artifactual or pathological) EEG
patterns. Results: We achieved the state of the art in all classification
problems, regardless the large variability of the datasets and the simple
architecture of the attention-enhanced models. We could also prove that,
depending on how the attention mechanism is applied and where the attention
layer is located in the model, we can alternatively leverage the information
contained in the time, frequency or space domain of the dataset. Conclusions:
with this work, we shed light over the role of different attention mechanisms
in the classification of normal and abnormal EEG patterns. Moreover, we
discussed how they can exploit the intrinsic relationships in the temporal,
frequency and spatial domains of our brain activity. Significance: Attention
represents a promising strategy to evaluate the quality of the EEG information,
and its relevance, in different real-world scenarios. Moreover, it can make it
easier to parallelize the computation and, thus, to speed up the analysis of
big electrophysiological (e.g., EEG) datasets.
