Topology-Aware Deep Learning Model Enhances EEG-Based Motor Imagery Decoding

Motor imagery electroencephalography (MI-EEG) is crucial for brain-computer interfaces, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, decoding MI-EEG signals is extremely challenging, and traditional methods overlook dependencies between spatiotemporal features and spectral-topological features. Now, researchers have developed a new topology-aware method that effectively captures the deep dependencies across different feature domains of EEG signals, ensuring accurate and robust decoding, paving the way for more brain-responsive technology.