Comparison of Attention-based Deep Learning Models for EEG Classification

TL;DR

This study compares various attention-based deep learning models for EEG classification, demonstrating their effectiveness and exploring how attention mechanisms can leverage different data domains to improve accuracy and interpretability.

Contribution

The paper introduces and evaluates three attention-enhanced deep learning models for EEG classification, highlighting how attention placement affects domain-specific information extraction.

Findings

Achieved state-of-the-art accuracy across datasets

Attention placement influences focus on time, frequency, or space domains

Attention mechanisms facilitate faster EEG data analysis

Abstract

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.