Brain Computer Interface Performance

Brain-computer interfaces (BCIs) aim to translate brain activity into commands for external devices, offering communication and control for individuals with severe disabilities. Current research focuses on improving BCI performance through optimized feature extraction (e.g., using regularized CSP and advanced channel selection), employing diverse deep learning architectures (including Riemannian geometry-based classifiers, EEGNet, EEG Conformers, and SPDNet variations), and integrating human input with reinforcement learning algorithms for shared autonomy. These advancements aim to enhance accuracy, reliability, and user experience, ultimately leading to more robust and clinically viable BCI systems.