Comparison of Attention-based Deep LearningModels for EEG Classification

Abstract

Deep Learning (DL) has recently shown promising classification performance in Electroencephalography (EEG) in many different scenarios. However, the complex reasoning of such models often prevent the user to explain their classification abilities. Attention, one of the most recent and influential ideas in DL, allows the models to learn which portions of the data are relevant to the final classification output. In this work, 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, including artifactual and pathological, EEG patterns in three different datasets. 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. Additionally, we proved 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 EEG dataset. Therefore, attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance for classification, in different real-world scenarios.

Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG

“…It provided satisfactory outcomes in a variety of EEG analysis, ranging from channel selection to classification of motor imagery (MI). Among other models, CNN was particularly successful to extract spatial and frequency features from EEG for speech classification, as reported in [28], to detect artifacts in EEG [21], as well as to recognize MI for BCI [25]. In the works by Dose [23] and Lee [24], the possibility of MI-EEG classification using a CNN architecture was explored.…”

“…However, they might score poorly in case of complex nonlinear EEG data [19]. However, deep DL has recently demonstrated promising results in decoding brain activity in several scenarios, e.g., sleep stage scoring [20], epileptic seizure detection [21], as well as hand movement classification [22]. Therefore, the aim of this work was to evaluate the performance of a newly proposed DL-based model, compared to the well-established sLDA and RF methods, in the classification of three different classes of movement, using two pre-recorded datasets.…”

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

“…In work [17], the author has discussed the difference be-tween machine learning techniques and deep learning method in distinguishing patients under antiepileptic drugs and those taking no medications, as well as between the two anticonvulsants. The method was validated on TUSZ dataset since it is the largest available dataset [5] The comparison invoked in the work [18] shows that a small difference exists between the used ML techniques and deep model in achieving a moderate accuracy rate for medication use detection.…”

“…Thus, an attention mechanism is introduced for channel importance learning and to pay different attention to various brain lobes. As abovementioned, the attention mechanism allows modeling of dependencies among EEG channels [12] and has shown success in some research topics [17] [32] [33].…”

Exercise therapy of mild cognitive impairment: EEG could enhance efficiency

“…In recent years, with the rapid development of neuroscience and medicine, some new non-pharmacological therapies of MCI have been proposed, like dual-task training (DTT) (Norouzi et al, 2019;Oliva et al, 2020;Kannan and Bhatt, 2021), resistance exercise (Hong et al, 2018), fall-resistance training (Bhatt et al, 2012), etc. Furthermore, electroencephalography (EEG)-based exercise therapies, such as open-loop EEG-based exercise therapy (Amjad et al, 2019b;Liao et al, 2019) and closed-loop EEG-based exercise therapy (Cisotto et al, 2021), have shown great potential for clinical application regarding MCI. This paper summarizes and analyzes the relevant literature on non-pharmacological therapies for MCI patients, including exercise and EEG-based exercise ones.…”