Pith Number

pith:667YQXJU

pith:2026:667YQXJU2SHVWA7PKJ7CXNUYRT

not attested not anchored not stored refs pending

From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG

Huilin Yao, Kaikai Wang, Lin Shu, Zhenghao Huang

AEMG pre-trains EMG signals as a cross-device physiological language using a contraction tokenizer to improve generalization in motor intent decoding.

arxiv:2605.03462 v2 · 2026-05-05 · cs.LG · cs.AI

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\pithnumber{667YQXJU2SHVWA7PKJ7CXNUYRT}

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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

Experiments demonstrate that AEMG improves the zero-shot leave-one-subject-out (LOSO) accuracy by 5.79-9.25% compared to six state-of-the-art baselines, and achieves more than 90% few-shot adaptation performance with only 5% of target user data.

C2weakest assumption

That EMG signals can be tokenized into a linguistic structure (words from contractions, sentences from temporal patterns) via NCT without losing critical discriminative information across heterogeneous devices and subjects.

C3one line summary

AEMG pre-trains EMG representations by treating neuromuscular signals as language via a novel tokenizer and cross-device vocabulary, yielding 5.79-9.25% zero-shot LOSO gains and over 90% few-shot performance with 5% target data.

Receipt and verification

First computed	2026-05-20T00:04:34.047349Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f7bf885d34d48f5b03ef527e2bb6988cd76eefc8cb421d03af67966419efb00c

Aliases

arxiv: 2605.03462 · arxiv_version: 2605.03462v2 · doi: 10.48550/arxiv.2605.03462 · pith_short_12: 667YQXJU2SHV · pith_short_16: 667YQXJU2SHVWA7P · pith_short_8: 667YQXJU

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/667YQXJU2SHVWA7PKJ7CXNUYRT \
  | 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: f7bf885d34d48f5b03ef527e2bb6988cd76eefc8cb421d03af67966419efb00c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c19c95cd9e7af4b3561a5e5c60116ec950be267e3c7496b61728f1700e48e14c",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-05T07:50:00Z",
    "title_canon_sha256": "813ee3cba9b0d3524837182f6d065e65f7d0d79be42c1f6d7085c22ddf505144"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.03462",
    "kind": "arxiv",
    "version": 2
  }
}