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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unreviewed preprint describing a novel attention mechanism (Adaptive Anisotropic Attention, or AAA) designed to decompose self-attention along the temporal and spatial axes of structured signals like EEG. The method improves balanced accuracy on six EEG downstream tasks relative to a dense baseline, with ablations supporting both paths and gated combination; however, the work is purely computational, lacks peer review, reports no clinical endpoints, and does not compare to established EEG processing or machine learning methods in clinical use.
Computational method development study with controlled experiments and ablations. EEG signals from six unspecified downstream tasks; separate audio spectrogram dataset. Intervention: Adaptive Anisotropic Attention (AAA) architecture with gated combination of temporal and spatial attention paths. Compared with: Dense self-attention baseline.
AXON model improves mean balanced accuracy over dense baseline on six EEG downstream tasks under linear probing and full fine-tuning Both temporal and spatial attention paths are necessary; weighted sum outperforms hard choice of one path Most benefit comes from gate learning different temporal/spatial balance at each network layer
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First-in-human or first-application machine learning architecture study on EEG with surrogate endpoints (balanced accuracy), unreviewed and lacking clinical validation or comparison to established methods.
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Dense self-attention treats all token pairs as equally plausible before learning, an interaction-isotropic prior that can be mismatched to structured signals. For structured, low signal-to-noise ratio (SNR) signals such as EEG, dependencies are organized along the electrode and time axes, and this uniform prior exposes each token to many irrelevant interactions. We introduce Adaptive Anisotropic Attention (AAA), which splits attention into two paths: a temporal path, where each token attends to the tokens of its own electrode across time, and a spatial path, where it attends to the tokens of the other electrodes at the same time step. A small gate predicts, for every token, a convex combination of the two path outputs: two non-negative weights that sum to one. On six EEG downstream tasks, the resulting model, AXON (AXis-factorized Operator Network), improves mean balanced accuracy over a dense baseline under both linear probing and full fine-tuning. We show that both paths (temporal and spatial) are necessary and that the weighted sum beats a hard choice of one path; most of the benefit comes from the gate learning a different temporal/spatial balance at each layer of the network. Controlled audio spectrogram experiments show that axis factorization transfers beyond EEG. These results suggest that aligning attention with the natural axes of structured signals provides a useful inductive bias.
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