2020/12/02 by Giulia Cisotto, Cisotto, Giulia, Alessio Zanga +9
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · #Artificial Intelligence (cs.AI) #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #Signal Processing (eess.SP) #cs.AI #cs.LG #eess.SP #electronic engineering #information engineering #q-bio.NC #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2012.01074
arxiv created 2020/12/02 · openalex publication_date 2020/12/02 · arxiv updated 2020/12/03 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.