Pith Number

pith:JPLYJKA4

pith:2026:JPLYJKA4KGZW7G4LHG32ABABMF

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EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts

Cuntai Guan, Guanxiang Huang, Motoaki Kawanabe, Qibin Zhao, Runhe Zhou, Shanglin Li, Xinliang Zhou, Yi Ding

EEG-MoCE assigns each modality to its own learnable-curvature hyperbolic expert and fuses them with curvature-aware weighting to capture hierarchical structures in brain signals.

arxiv:2604.12579 v3 · 2026-04-14 · cs.LG

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

EEG-MoCE, a novel hyperbolic mixture-of-curvature experts framework, assigns each modality to an expert in a learnable-curvature hyperbolic space and uses curvature-aware fusion to achieve state-of-the-art performance on benchmark datasets for emotion recognition, sleep staging, and cognitive assessment.

C2weakest assumption

That EEG and associated modalities exhibit hierarchical structures best captured by hyperbolic geometry with independently learnable curvatures per modality, and that the curvature-aware fusion strategy reliably emphasizes modalities with richer hierarchical information without introducing instability or overfitting.

C3one line summary

EEG-MoCE assigns each modality to a learnable-curvature hyperbolic expert and applies curvature-aware fusion to achieve state-of-the-art results on emotion recognition, sleep staging, and cognitive assessment benchmarks.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-01T01:02:39.637363Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4bd784a81c51b36f9b8b39b7a00401617fc848a563fe510bb1a36f0e6e3174ac

Aliases

arxiv: 2604.12579 · arxiv_version: 2604.12579v3 · doi: 10.48550/arxiv.2604.12579 · pith_short_12: JPLYJKA4KGZW · pith_short_16: JPLYJKA4KGZW7G4L · pith_short_8: JPLYJKA4

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JPLYJKA4KGZW7G4LHG32ABABMF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 4bd784a81c51b36f9b8b39b7a00401617fc848a563fe510bb1a36f0e6e3174ac

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "646f6c7133462083e17a4d8a349f9d85760e78bd1a2d289fe4b2504f5b74175c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-14T11:03:51Z",
    "title_canon_sha256": "9af270117c5899c81d5a7cff4e6ece4dbccbf0bb23d3a8e9175f2e6483a1aa0f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.12579",
    "kind": "arxiv",
    "version": 3
  }
}