Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Abibullaev, Berdakh; Keutayeva, Aigerim; Zollanvari, Amin

doi:10.1109/access.2023.3329678

Cited by 8 publications

(1 citation statement)

References 223 publications

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“…Attention mechanisms have been extensively employed in various EEG signal analyses [24,25]. They enable models to focus on different parts of the input, intelligently assigning weights to different inputs based on the specific task, demonstrating excellent performance across a variety of tasks [26]. Its essence lies in computing attention coefficients for corresponding Q (Query), K (Key), and V (Value) [27], as shown in (1), thereby assigning weights to different information.…”

Section: ) Attention Module Selectionmentioning

confidence: 99%

A Study of Unilateral Upper Limb Fine Motor Imagery Decoding Using Frequency-Band Attention Network

Shi,

Gu,

et al. 2024

IEEE Access

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Brain-computer interface (BCI) based motor imagery (MI) can assist stroke patients in upper limb rehabilitation and help restore motor function to a certain extent. However, the classical MI paradigm distinguishes different limbs and cannot effectively meet the needs of upper limb rehabilitation training for patients. Therefore, this paper designed a new paradigm for three motor imagery actions targeting different joints of the unilateral upper limb, and electroencephalogram (EEG) data from 20 healthy participants were collected for research analysis. A deep neural network model combining an attention mechanism for multiple frequency bands and a deep convolutional network were proposed to adaptively assign weight to the EEG data in different frequency bands. Then feature extraction was performed for each frequency band to learn further and to classify features. This model can obtain an average accuracy of 69.2% for the subjectindependent case with the triple classification in the designed fine motor imagery (FMI) dataset, which is better than other controlled methods. Furthermore, ablation experiments were conducted for each module, demonstrating the effectiveness of each module. These results manifest the feasibility of our proposed method and the potential of FMI paradigm for BCI, providing a new training tool for upper limb rehabilitation after stroke.

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Section: ) Attention Module Selectionmentioning

confidence: 99%

A Study of Unilateral Upper Limb Fine Motor Imagery Decoding Using Frequency-Band Attention Network

Shi,

Gu,

et al. 2024

IEEE Access

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EEG-based emotion recognition systems; comprehensive study

Hamzah,

Abdalla

2024

Heliyon

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Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

Keutayeva,

Abibullaev

2024

IEEE Access

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This work reviews the critical challenge of data scarcity in developing Transformerbased models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), specifically focusing on Motor Imagery (MI) decoding. While EEG-BCIs hold immense promise for applications in communication, rehabilitation, and human-computer interaction, limited data availability hinders the use of advanced deep-learning models such as Transformers. In particular, this paper comprehensively analyzes three key strategies to address data scarcity: data augmentation, transfer learning, and the inherent attention mechanisms of Transformers. Data augmentation techniques artificially expand datasets, enhancing model generalizability by exposing them to a wider range of signal patterns. Transfer learning utilizes pre-trained models from related domains, leveraging their learned knowledge to overcome the limitations of small EEG datasets. By thoroughly reviewing current research and methodologies, this work underscores the importance of these strategies in overcoming data scarcity. It critically examines the limitations imposed by limited datasets and showcases potential solutions being developed to address these challenges. This comprehensive survey, focusing on the intersection of data scarcity and technological advancements, aims to provide a critical analysis of the current state-of-the-art in EEG-BCI development. By identifying research gaps and suggesting future directions, the paper encourages further exploration and innovation in this field. Ultimately, this work aims to contribute to the advancement of more accessible, efficient, and precise EEG-BCI systems by addressing the fundamental challenge of data scarcity.

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Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

Cited by 8 publications

References 223 publications

A Study of Unilateral Upper Limb Fine Motor Imagery Decoding Using Frequency-Band Attention Network

A Study of Unilateral Upper Limb Fine Motor Imagery Decoding Using Frequency-Band Attention Network

EEG-based emotion recognition systems; comprehensive study

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

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