Benchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models

Researchers benchmarked five positional encoding strategies for transformer-based EEG foundation models, finding that no single approach universally outperforms across different brain-computer interface tasks. Spherical Positional Encoding excels at motor imagery classification while Asymmetric Conditional Positional Encoding shows more consistent cross-task performance, suggesting optimal encoding strategies are task-dependent rather than universally applicable.

This research addresses a fundamental architectural challenge in adapting transformer models to electroencephalography data. While transformers have revolutionized natural language processing and vision tasks, their application to EEG signals requires solving the positional encoding problem differently than traditional domains. EEG electrodes form a fixed spatial topology on the scalp, unlike sequential tokens in text or grid positions in images, necessitating specialized encoding approaches that preserve this spatial information.

The study's comparative evaluation of five positional encoding strategies within the CBraMod backbone provides empirical guidance for practitioners developing EEG-based brain-computer interfaces. The finding that Spherical Positional Encoding performs exceptionally well for motor imagery tasks but poorly for emotion recognition reveals important task-specific characteristics of neural activity patterns. This suggests that motor imagery may benefit from more explicit spatial structure preservation, while emotion recognition tasks may distribute relevant information differently across electrode locations.

The broader implications affect development of robust EEG foundation models that can generalize across diverse applications. Brain-computer interface applications span medical diagnostics, neurological disorder monitoring, and assistive technologies—domains where transfer learning and model reusability significantly reduce development costs. The task-dependent nature of optimal encoding strategies suggests that practitioners cannot simply apply pre-trained models universally but instead require either task-specific fine-tuning or adaptive encoding mechanisms.

Future research should explore whether hybrid encoding approaches or learnable positional encodings could achieve more consistent performance. Understanding how electrode topology interacts with different neural decoding tasks could accelerate development of more generalizable foundation models for brain-computer interfaces.

• →No single positional encoding strategy consistently outperforms across EEG tasks, requiring task-specific optimization.
• →Spherical Positional Encoding excels for motor imagery classification but underperforms on emotion recognition tasks.
• →Asymmetric Conditional Positional Encoding demonstrates superior cross-task stability compared to other tested strategies.
• →Electrode spatial topology requires specialized positional encoding different from text or vision transformer approaches.
• →Task-dependent encoding selection is critical for developing generalizable EEG foundation models in brain-computer interfaces.