Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces

• Published in: Computers in biology and medicine 2024-08, Vol.178, p.108705, Article 108705
• Main Authors: Pfeffer, Maximilian Achim, Ling, Steve Sai Ho, Wong, Johnny Kwok Wai
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Biochips; Brain; Brain - physiology; Brain research; Brain-computer interface; Brain-Computer Interfaces; Classification; Cognitive tasks; Communication; Computer applications; Decision making; Deep learning; EEG; Electroencephalography; Electroencephalography - methods; Human-computer interface; Humans; Implants; Interfaces; Language; Literature reviews; Machine learning; Mental task performance; Natural language processing; Neural networks; Noise reduction; Post traumatic stress disorder; Real time; Reviews; Signal analysis; Signal processing; Signal Processing, Computer-Assisted; Signal to noise ratio; Transformer
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108705

This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems. •Transformer-based EEG signal encoders show promise for improving BCI performance.•BCI research lags behind in adopting transformer models despite the NLP boom.•Optimizing transformer architecture could lead to efficient end-to-end BCI processing pipelines.•The reliability of transformer-based models in multi-class classification requires exploration.