arXiv:2512.19097v3 Announce Type: replace Abstract: Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is difficult because electrode layouts, anatomical coverage, referencing schemes, and recording conditions vary across patients and centers. We introduce DIVER-1, a self-supervised iEEG foundation model for variable-input recordings that combines any-variate electrode-time attention, spatio-temporal resampling, input-conditioned positional embeddings, and multi-domain masked reconstruction without assuming a fix
The development of DIVER-1 emerges due to advancements in self-supervised learning for variable-input data, addressing the inherent complexities and variability of intracranial EEG recordings across different patients and medical centers.
This development is crucial for strategic readers as it signifies a leap in neurotechnology, enabling the creation of transferable and reusable representations of human neural activity, which can accelerate neurological research, diagnostics, and therapeutic interventions.
The ability to generate robust, generalizable representations from highly variable iEEG data changes how brain activity can be analyzed and understood, potentially leading to more accurate and personalized neurological treatments.
- · Neurology researchers
- · Medical device manufacturers (iEEG technology)
- · AI healthcare platforms
- · Patients with neurological disorders
- · Traditional, manual iEEG analysis methods
- · Companies relying on proprietary, non-interoperable brain data systems
Improved understanding and mapping of complex brain functions and dysfunctions.
Accelerated development of brain-computer interfaces and neuro-prosthetics capable of adapting to diverse neural signals.
Ethical and privacy debates around access to and interpretation of highly personalized neural data, potentially impacting regulation and public perception.
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Read at arXiv cs.LG