Pith Number

pith:TFVYYAJU

pith:2026:TFVYYAJUT3BEELW4S6BAFUG6OE

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DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features

Bui Thanh Tung, Hung Cao, Huy-Dung Han, Khuong Vo, Manoj Vishwanath, Minh Long Ngo, Ngoc-Son Nguyen, Nguyen Quang Linh, Nguyen Thanh Vinh, Thinh Nguyen-Quang

DeepTokenEEG uses tokenized EEG features in a lightweight model to achieve up to 100% accuracy in Alzheimer's classification, outperforming prior methods by 1.41-15.35%.

arxiv:2605.15009 v1 · 2026-05-14 · cs.LG

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\pithnumber{TFVYYAJUT3BEELW4S6BAFUG6OE}

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3 Author claim open · sign in to claim

4 Citations 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

a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. ... achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset.

C2weakest assumption

That the 100% accuracy on specific frequency bands generalizes beyond the 274-subject dataset (180 AD, 94 controls) and is not due to overfitting, data leakage, or unaccounted artifacts in the EEG recordings.

C3one line summary

DeepTokenEEG uses tokenized EEG features in a lightweight model to achieve up to 100% accuracy in Alzheimer's classification, outperforming prior methods by 1.41-15.35%.

Receipt and verification

First computed	2026-05-17T23:38:54.803582Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

996b8c01349ec2422edc978202d0de71360e1e031776eb3efdb856bbcb9a2e10

Aliases

arxiv: 2605.15009 · arxiv_version: 2605.15009v1 · doi: 10.48550/arxiv.2605.15009 · pith_short_12: TFVYYAJUT3BE · pith_short_16: TFVYYAJUT3BEELW4 · pith_short_8: TFVYYAJU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TFVYYAJUT3BEELW4S6BAFUG6OE \
  | 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: 996b8c01349ec2422edc978202d0de71360e1e031776eb3efdb856bbcb9a2e10

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c269306c3d04e7b348a6e0f90d22fc77dde3af7d1cdf33e25d9311a9e140bab2",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T16:10:03Z",
    "title_canon_sha256": "bf7fc9542c5cd80947c8d94a3ee32d30c998db742dbfbcf263b35f411cf2bb2f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15009",
    "kind": "arxiv",
    "version": 1
  }
}