Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
arXiv:2606.30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of temporal feature extractors and whether pretrained time-series foundation models (TSFMs) can be effectively transferred to this setting remains underexplored. We conduct a controlled comparison of three temporal feature extraction strategies, including a linear baseline, a convolutional encoder, and a frozen pretrained TSFM (MOMENT), within a unified EEG foundation model. We evaluate their impact on rep
The proliferation of foundation models across domains makes their application to specialized time-series data like EEG an active area of research, seeking to leverage existing general-purpose models.
This research explores fundamental architectural choices for EEG foundation models, which are critical for advancing brain-computer interfaces, neurological diagnostics, and potentially more general AI agents interacting with biological data.
Our understanding of how best to design temporal feature extractors for sensitive biomedical time-series data using existing AI architectures is refined, indicating a path toward more robust and generalizable EEG models.
- · AI researchers
- · Healthcare AI companies
- · Neurology
- · Brain-computer interface developers
- · Traditional EEG analysis methods
- · Companies slow to adopt foundation models
Improved performance and generalizability of EEG analysis models through optimized temporal feature extraction.
Accelerated development of robust brain-computer interfaces and neuroprosthetics, leveraging advanced EEG understanding.
Enhanced ability for AI systems to interpret and interact with complex biological signals, blurring lines between digital and biological intelligence.
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Read at arXiv cs.AI