DS-LCCA: Differentially Supervised Lightness–Chromaticity Complementary Alignment for Neural Visual Decoding

Decoding visual representations from brain signals helps reveal how the brain represents visual information and enables new forms of brain–computer interaction. However, existing approaches to visual supervision adaptation do not explicitly account for the distinct spatial sensitivities of human vision to Lightness and Chromaticity variations, potentially limiting neural–visual alignment. To address this limitation, we propose Differentially Supervised Lightness-Chromaticity Complementary Alignment (DS-LCCA), which constructs component-specific visual supervision in CIELUV space. The Lightness branch reweights multiscale discrete wavelet coefficients to attenuate spatial detail, whereas the Chromaticity branch applies Gaussian smoothing to suppress fine-scale Chromaticity variations. Each transformation preserves the remaining coordinates before conversion back to RGB. Four independent branches learn neural–visual alignment through symmetric contrastive objectives. Three Residual Projection Encoders align EEG or MEG recordings with CLIP ResNet-50 features of the original and transformed images for retrieval and classification. A dedicated ATM–ViT-H-14 branch learns original-image representations for reconstruction. Its neural embeddings condition a diffusion prior to produce the primary visual condition, while linear projections of the Lightness and Chromaticity embeddings provide auxiliary conditions. Separate IP-Adapters jointly condition a pretrained SDXL generator, and the auxiliary projections are optimized through the generation loss. Extensive experiments on THINGS-EEG and THINGS-MEG demonstrate that DS-LCCA outperforms state-of-the-art methods across retrieval, classification, and reconstruction. The code is available at https://anonymous.4open.science/r/DS-LCCA-4CB5.