Transformer-based EEG Decoding: A Survey
Haodong Zhang, Hongqi Li

TL;DR
This survey reviews recent developments in applying Transformer models to EEG decoding, highlighting architectural innovations, hybrid models, and future challenges in brain-computer interface research.
Contribution
It provides a comprehensive overview of Transformer-based EEG decoding methods, including architecture evolution, hybrid models, and customized Transformer structures, guiding future research directions.
Findings
Transformer models effectively handle EEG sequential data.
Hybrid architectures improve decoding performance.
Customized Transformers address specific EEG decoding challenges.
Abstract
Electroencephalography (EEG) is one of the most common signals used to capture the electrical activity of the brain, and the decoding of EEG, to acquire the user intents, has been at the forefront of brain-computer/machine interfaces (BCIs/BMIs) research. Compared to traditional EEG analysis methods with machine learning, the advent of deep learning approaches have gradually revolutionized the field by providing an end-to-end long-cascaded architecture, which can learn more discriminative features automatically. Among these, Transformer is renowned for its strong handling capability of sequential data by the attention mechanism, and the application of Transformers in various EEG processing tasks is increasingly prevalent. This article delves into a relevant survey, summarizing the latest application of Transformer models in EEG decoding since it appeared. The evolution of the model…
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