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

doi:10.1016/j.compbiomed.2024.108705

Computers in Biology and Medicine

2024

DOI: 10.1016/j.compbiomed.2024.108705

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

Maximilian Achim Pfeffer,

Steve Sai Ho Ling,

Johnny Kwok Wai Wong

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2024

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Cited by 3 publications

References 91 publications

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Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Liang,

Wang,

et al. 2024

Journal of Computational Design and Engineering

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Cutting tool remaining life during machining processes is essential for achieving sustainable production. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilising multi-source data to address these challenges effectively. The key innovations include: (1) The proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardised data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life, best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite Element Analysis (FEA) is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimisation mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64% and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in enhancing the operational efficiency and reliability of manufacturing systems is validated through advanced data integration and deep learning techniques.

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Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Liang,

Wang,

et al. 2024

Journal of Computational Design and Engineering

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Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers

Zhang,

Deng,

Ling

2024

Sensors

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The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.

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EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain–Computer Interfaces

Nguyen,

Oyefisayo,

Pfeffer

et al. 2024

Signals

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In brain–computer interface motor imagery (BCI-MI) systems, convolutional neural networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. EEG-TCNTransformer achieves an accuracy of 83.41% without the application of bandpass filtering.

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

Cited by 3 publications

References 91 publications

Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers

EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain–Computer Interfaces

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