Generación mutua para el desafío interdominio en la clasificación de imágenes motoras y la predicción de recuperación...

The accumulating body of research indicates that Motor Imagery (MI)-BCIs have the potential to enhance the quality of life for individuals with disabilities and to advance our understanding of brain function and rehabilitation strategies. Among these diseases, stroke is the leading cause of long-term motor disability across the globe, thereby underscoring the need for innovative rehabilitation strategies, such as MI-BCI technologies. In contrast with these expectations, the majority of existing research is built upon data obtained from healthy subjects. The construction of effective classification models for Motor Imagery tasks in patients with brain diseases, particularly stroke, remains a significant challenge. The lateralization of the left and right hemispheres is more pronounced in patients who have suffered a stroke than in healthy individuals. Moreover, the specific locations of lesions and the regions of influence result in significant variations in the electroencephalogram (EEG) data of patients with different hemiplegic sides. This paper explores the potential of generative models in addressing the issue of domain differences arising from different hemiplegic sides EEG data. Furthermore, this paper circumvents the potential adverse effects of rigorous optimization of low-quality samples on model performance through the utilization of label softening algorithm. Two MI-EEG datasets of stroke patients performing Motor Imagery tasks are used to validate our method. In comparison to both classical machine learning methods and those state-of-the-art models for MI classification, the classification model in this paper achieves a noticeable performance improvement in different data partitioning strategies, including subject-dependent and subject-independent scenarios. Each sub-module, and each designed loss function, contributes to the final performance growth. In addition, this paper also investigates the potential of the proposed framework for predicting a patient's level of functional recovery. Our findings indicate that the addition of a prediction layer to the proposed model enables the accurate prediction of functional recovery level in stroke patients. The source code is available at https://github.com/arrogant-R/MutualGeneration.