ā” Quick Summary
This study introduces a novel approach using self-attention convolutional neural networks (SACNN) combined with partial prior transfer learning (PPTL) to enhance the decoding of motor imagery tasks in stroke patients. The proposed method achieved a remarkable classification accuracy of 55.4% in four types of motor imagery tasks, significantly outperforming existing algorithms.
š Key Details
- š Participants: 22 stroke patients
- š§© Tasks: Four types of motor imagery tasks
- āļø Technology: Self-attention convolutional neural network (SACNN) with partial prior transfer learning (PPTL)
- š Performance: Classification accuracy of 55.4%Ā±0.17
- š Publication: Sci Rep, 2024
š Key Takeaways
- š§ MI-BCI can help stroke patients activate motor regions in the brain.
- š SACNN-PPTL improves classification performance for motor imagery tasks.
- š Significant improvement in decoding performance compared to traditional methods.
- š Experimental results showed a statistically significant accuracy increase (Pā<ā0.05).
- š” The model incorporates temporal, spatial, and feature generalization modules.
- š„ Potential for rehabilitation of unilateral upper limb function in stroke patients.
- š Comparison algorithms included five backbone networks and three training modes.
š Background
Stroke rehabilitation is a critical area of research, particularly in enhancing motor function recovery. Motor imagery-based brain-computer interfaces (MI-BCI) have emerged as a promising tool to assist stroke patients in activating motor regions of the brain. However, the complexity of decoding these signals, especially during multi-task scenarios, poses significant challenges. This study aims to address these challenges through innovative machine learning techniques.
šļø Study
The research focused on developing a new method for EEG decoding in stroke patients using a self-attention convolutional neural network (SACNN) integrated with partial prior transfer learning (PPTL). The study involved 22 stroke patients performing four types of motor imagery tasks, aiming to enhance the classification performance of the MI-BCI system.
š Results
The SACNN-PPTL model demonstrated a classification accuracy of 55.4%Ā±0.17 across the four motor imagery tasks. This performance was significantly better than that of the comparison algorithms, indicating the effectiveness of the proposed method in improving the decoding of EEG signals for stroke rehabilitation.
š Impact and Implications
The findings from this study have the potential to significantly advance the field of stroke rehabilitation. By improving the decoding performance of MI tasks, the SACNN-PPTL model could facilitate more effective rehabilitation strategies for patients with unilateral upper limb impairments. This could lead to enhanced recovery outcomes and a better quality of life for stroke survivors.
š® Conclusion
This research highlights the transformative potential of machine learning in the realm of stroke rehabilitation. The introduction of SACNN-PPTL represents a significant step forward in the development of brain-computer interfaces, paving the way for more effective rehabilitation techniques. Continued exploration in this area is essential for further advancements in patient care and recovery.
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Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.
Abstract
The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients’ MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%Ā±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (Pā<ā0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb.
Author: [‘Ma J’, ‘Ma W’, ‘Zhang J’, ‘Li Y’, ‘Yang B’, ‘Shan C’]
Journal: Sci Rep
Citation: Ma J, et al. Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients. Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients. 2024; 14:28170. doi: 10.1038/s41598-024-79202-8