Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification

Elnaz Vafaei ¹, Mohammad Hosseini ²

⁰Department of Psychology, Northeastern University, Boston, MA 02115, USA.

Sensors (basel, Switzerland) +

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March 17, 2025

View abstract on PubMed

Summary

Transformers excel in analyzing long sequences, significantly advancing electroencephalography (EEG) research. This review explores transformer models, applications like seizure detection, and solutions for EEG data challenges.

Area Of Science

Neuroscience
Machine Learning
Signal Processing

Background

Transformers demonstrate superior sequence encoding capabilities, outperforming traditional machine learning methods.
A surge in transformer-based models for electroencephalography (EEG) analysis necessitates a comprehensive review of their architectures and applications.
Existing literature highlights the need for structured insights into transformer utilization within EEG studies.

Purpose Of The Study

To explore four major transformer architectures (Time Series Transformer, Vision Transformer, Graph Attention Transformer, hybrid models) applied to EEG analysis.
To categorize recent transformer-based EEG studies by application, focusing on motor imagery classification, emotion recognition, and seizure detection.
To identify and review challenges in applying transformers to EEG data, alongside data augmentation and transfer learning solutions.

Main Methods

Systematic review of recent transformer-based electroencephalography (EEG) studies.
Categorization of studies based on transformer architecture and application domain.
Analysis of challenges and proposed solutions, including data augmentation and transfer learning.

Main Results

Identification and discussion of four key transformer architectures and their variants in EEG analysis.
Classification of transformer applications in EEG, with emphasis on motor imagery, emotion recognition, and seizure detection.
Overview of challenges and emerging solutions for transformer implementation in EEG.

Conclusions

Transformers offer powerful tools for EEG analysis, with diverse architectures and applications.
Addressing challenges like data augmentation and transfer learning is crucial for effective transformer deployment in EEG.
This review provides a roadmap for researchers exploring transformer architectures for EEG data.

Keywords:

brain–computer interface (BCI) electroencephalography (EEG) emotion recognition graph attention transformer motor imagery classification seizure detection transformers vision transformer