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Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces.
Pfeffer, Maximilian Achim; Ling, Steve Sai Ho; Wong, Johnny Kwok Wai.
Afiliación
  • Pfeffer MA; Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia. Electronic address: Max.Pfeffer@uts.edu.au.
  • Ling SSH; Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia. Electronic address: Steve.Ling@uts.edu.au.
  • Wong JKW; Faculty of Design, Architecture and Building, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia. Electronic address: Johnny.Wong@uts.edu.au.
Comput Biol Med ; 178: 108705, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38865781
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Electroencefalografía / Interfaces Cerebro-Computador Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Señales Asistido por Computador / Electroencefalografía / Interfaces Cerebro-Computador Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos