mlsanityData to 7 Oct 2026

Human-Aligned Image Models Improve Visual Decoding from the Brain

arXiv 2502.03081 ↗First posted 5 Feb 2025cs.CV cs.LGcited by 7

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Abstract (arXiv)

Decoding visual images from brain activity has significant potential for advancing brain-computer interaction and enhancing the understanding of human perception. Recent approaches align the representation spaces of images and brain activity to enable visual decoding. In this paper, we introduce the use of human-aligned image encoders to map brain signals to images. We hypothesize that these models more effectively capture perceptual attributes associated with the rapid visual stimuli presentations commonly used in visual brain data recording experiments. Our empirical results support this hypothesis, demonstrating that this simple modification improves image retrieval accuracy by up to 21% compared to state-of-the-art methods. Comprehensive experiments confirm consistent performance improvements across diverse EEG architectures, image encoders, alignment methods, participants, and brain imaging modalities

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 48 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

26 of 48 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2025Foundation Models for Cross-Domain EEG Analysis Application: A Surveycited by 4
  2. 2026What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrievalcited by 1
  3. 2026Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decodingcited by 0
  4. 2026Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decodingcited by 0
  5. 2026Dual-Stream EEG Decoding for 3D Visual Perceptioncited by 0
  6. 2026What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignmentcited by 0
  7. 2026The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrievalcited by 0