---
title: "SCORE：标签自由跨受试者 EEG 到图像检索的坐标恢复方法"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.19134v1"
permalink: "https://feed.astralor.com/items/itm_425dd9dbbbd95323ab8b32c1"
published: "2026-08-19T17:27:29.000Z"
score: 78
channel: "ai"
tags: ["SCORE","图像检索","脑电信号","视觉解码","零样本适配"]
---

# SCORE：标签自由跨受试者 EEG 到图像检索的坐标恢复方法

## AI 点评

> 这是一篇把问题拆得很清楚的工作，它没有堆砌复杂网络，而是抓住了跨受试者坐标偏移这一核心症结，用坐标对齐加无标签适配就实现了大跨度的性能提升。对于做脑机接口或跨用户检索的研究者来说，这套方法很值得直接借鉴；对产品向的团队，它意味着离“开箱即用”的神经检索又近了一步。

## 速览

脑电波到图像检索在神经通信领域潜力巨大，但新用户缺乏标定数据时性能大幅下降。本文分析了 EEG 特征，发现不同受试者概念关系相似但坐标方向不同，据此提出受试者坐标恢复（SCORE）框架。训练阶段将源受试者 EEG 对齐到图像空间，并模拟未见受试者恢复；部署阶段利用 hubness 校正匹配选择可靠 landmark，估计正交变换恢复目标坐标，全程无需源数据或目标标签。在 THINGS-EEG2 和 Alljoined-1.6M 上，SCORE 以 200 路检索显著超越最强基线，为跨用户视觉解码提供了实用高效方案。

## 关键点

- 发现不同受试者的 EEG 特征存在共享概念结构但坐标方向不同，解释了跨受试者性能差距的根源。
- 提出 SCORE 框架，部署时无需目标标签或编码器更新，仅靠坐标对齐即可完成零样本跨受试者检索。
- 在 THINGS-EEG2 和 Alljoined-1.6M 两个基准上，Top-1 准确率分别达到 53.23% 和 12.01%，较最强基线提高 17.45 和 3.08 个百分点。

## 全文

Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potential for neural communication. However, current EEG-to-image retrieval methods perform far below their within-subject counterparts for new users without labeled calibration, limiting real-world deployment. To understand this gap, we analyze EEG features across subjects and find that different subjects preserve similar relationships among concepts but express them along different coordinate directions. We therefore propose Subject Coordinate Recovery (SCORE), a target label-free framework combining recovery-aware source training with coordinate alignment at deployment. During training, SCORE aligns source subject EEG with a common image space and simulates unseen-subject recovery through source-only episodes. At deployment, with both encoders frozen, SCORE selects reliable EEG-image landmarks through hubness-corrected matching and estimates an orthogonal transformation to recover target EEG coordinates without source data or target labels. In 200-way retrieval on two public benchmarks, SCORE outperforms the unadapted baseline for every target subject and achieves the best overall accuracy. It reaches 53.23%/83.55% and 12.01%/32.16% Top-1/Top-5 on THINGS-EEG2 and Alljoined-1.6M, respectively, surpassing the strongest baselines by 17.45/15.70 and 3.08/4.62 percentage points. Without target labels or encoder updates, SCORE brings brain-based visual decoding closer to robust, practical, low-latency deployment across users.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_425dd9dbbbd95323ab8b32c1