Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models

ArXi:2605.29754v1 Announce Type: new Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Supervised EEG decoding models often struggle to generalize across tasks, subjects, and datasets, motivating transformer-based EEG foundation models trained with self-supervised learning. Since transformers are permutation-invariant, they require explicit positional information.