Transformer-based hybrid systems to combat BCI illiteracy.

This study addresses the challenge of enhancing Brain-Computer Interfaces (BCIs), focusing on low Signal-to-Noise Ratios and "BCI illiteracy" often affecting up to 20% of users. Transformer-based models show promise but remain underexplored. Three experiments were conducted. Experiment A assessed the performance of architectures combining Convolutional and Transformer Blocks for binary Motor Imagery (MI) classification. Experiment B introduced a hybrid system, refining both block types and adding a Noise Focus Block to infuse Stochastic Noise, enhancing multi-class classification robustness. Experiment C evaluated the emerging architectures on 106 subjects, focusing on robustness across weak and strong learners. In Experiment A, the best networks achieved a validation accuracy of 0.914 and a loss of 0.146 (p=0.000967, F=12.675). In Experiment B, the proposed architecture improved multi-class MI classification to 84.5% on Dataset II, significantly improving performance for BCI-illiterate users. Experiment C showed a Kappa >83%, reduced standard deviation, and a highest validation accuracy of 88.69% across all individuals. The hybrid integration of Transformers, CNNs, and Noise-Resonance-based layers significantly enhances classification performance, particularly for weak BCI learners. Further research is recommended to optimize hybrid system architectures and hyperparameter settings to overcome current limitations in BCI performance.