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Pfeffer MA, Ling SSH, Wong JKW. Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces. Comput Biol Med 2024; 178:108705. [PMID: 38865781 DOI: 10.1016/j.compbiomed.2024.108705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 05/01/2024] [Accepted: 06/01/2024] [Indexed: 06/14/2024]
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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Affiliation(s)
- Maximilian Achim Pfeffer
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Steve Sai Ho Ling
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Johnny Kwok Wai Wong
- Faculty of Design, Architecture and Building, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia.
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Zhao R, Yue T, Xu Z, Zhang Y, Wu Y, Bai Y, Ni G, Ming D. Electroencephalogram-based objective assessment of cognitive function level associated with age-related hearing loss. GeroScience 2024; 46:431-446. [PMID: 37273160 PMCID: PMC10828275 DOI: 10.1007/s11357-023-00847-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023] Open
Abstract
Age-Related Hearing Loss (ARHL) is a common problem in aging. Numerous longitudinal cohort studies have revealed that ARHL is closely related to cognitive function, leading to a significant risk of cognitive decline and dementia. This risk gradually increases with the severity of hearing loss. We designed dual auditory Oddball and cognitive task paradigms for the ARHL subjects, then obtained the Montreal Cognitive Assessment (MoCA) scale evaluation results for all the subjects. Multi-dimensional EEG characteristics helped explore potential biomarkers to evaluate the cognitive level of the ARHL group, having a significantly lower P300 peak amplitude coupled with a prolonged latency. Moreover, visual memory, auditory memory, and logical calculation were investigated during the cognitive task paradigm. In the ARHL groups, the alpha-to-beta rhythm energy ratio in the visual and auditory memory retention period and the wavelet packet entropy value within the logical calculation period were significantly reduced. Correlation analysis between the above specificity indicators and the subjective scale results of the ARHL group revealed that the auditory P300 component characteristics could assess attention resources and information processing speed. The alpha and beta rhythm energy ratio and wavelet packet entropy can become potential indicators to determine working memory and logical cognitive computation-related cognitive ability.
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Affiliation(s)
- Ran Zhao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, 300072, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, 300392, China
| | - Tao Yue
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China
| | - Zihao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China
| | - Yunqi Zhang
- School of Education, Tianjin University, Tianjin, China
| | - Yubo Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China.
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, 300072, China.
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, 300392, China.
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China.
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, 300072, China.
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, 300392, China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, 300072, China
- Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, 300392, China
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Schmoigl-Tonis M, Schranz C, Müller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci 2023; 17:1251690. [PMID: 37920561 PMCID: PMC10619676 DOI: 10.3389/fnhum.2023.1251690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.
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Affiliation(s)
- Mathias Schmoigl-Tonis
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Christoph Schranz
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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