Dynamic Cognitive Prior Generation for Robust EEG-to-Image Decoding

Abstract

One of the key challenges in brain computer interfaces (BCIs) is to understand the visual perceptual content (VPC) of non-invasive electroencephalography (EEG) signals with high accuracy, without the aid of brain mapping techniques, which is hindered by high inter-subject variations and the non-stationary nature of neural responses. Current methods, including NeuroBridge, use handcrafted, fixed, subject-agnostic transformations of perceptual variance called Cognitive Prior Augmentation (CPA). These static priors, however, have little capability for modelling the dynamic changes of cognition states and individual brain properties, which severely constrain across-subjects generalization. We introduced Dynamic Cognitive Prior Generation (DCPG) a new framework, which can be learned and is prototype based to adaptively generate priors instead of heuristic augmentations. Our approach distills the subject-specific cognitive priors by modelling the attention to a common bank of prototype representations, based on a given EEG trial and based on a learnable subject embedding. Using feature modulation, DCPG can adaptively calibrate the representation of EEG before semantic projection, thus reducing the domain shift between different subjects effectively. It was shown that DCPG can be used to significantly increase the accuracy of inter-subject retrieval on the THINGS-EEG dataset with an accuracy improvement of +3.0% while adding 4.9% more parameters compared to NeuroBridge. This framework is the new state-of-the-art in the field of robust EEG-image decoding, with results in a variety of populations. REFERENCES [1] W. Zhang, S. Wang, Y. Su, X. Li, C. Zhang, and S. Zhong, "Neurobridge: Bio-inspired self-supervised eeg-to-image decoding via cognitive priors and bidirectional semantic alignment," in Proc. AAAI Conf. Artif. Intell., vol. 40, no. 21, pp. 18028-18036, 2026. [2] K. Ahmed, H. A. Mahmoud, H. M. El Hadad, and Y. Afifi, "Generative AI for the reconstruction of visual stimuli from functional magnetic resonance imaging (fMRI) signals," J. Big Data, 2026. [3] H. Yu, Q. Mu, C. Liu, S. Wang, and J. Sun, "Technical system of electroencephalography-based brain–computer interface: Advances, applications, and challenges," Neural Regener. Res., vol. 21, no. 9, pp. 3885-3907, 2026. [4] X. T. Tran et al., "Inter-and Intra-Subject Variability in EEG: A Systematic Survey," arXiv:2602.01019, 2026. [5] S. Shukla et al., "A survey on bridging EEG signals and generative AI: from image and text to beyond," arXiv:2502.12048, 2025. [6] B. Abibullaev, A. Keutayeva, and A. Zollanvari, "Deep learning in EEG-based BCIs: A comprehensive review of transformer models, advantages, challenges, and applications," IEEE Access, vol. 11, pp. 127271-127301, 2023. [7] M. Hemati, C. Rowley, E. Deem, and L. Cattafesta, "De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets," Theor. Comput. Fluid Dyn., vol. 31, no. 4, 2017. [8] Y. Prabhu, "Unveiling Bias in Multimodal Models," Ph.D. dissertation, 2025. [9] L. Guo and W. Zhang, "Geospatial sentiment analytics for hospitality management: predicting investment returns on service attributes across urban micro-zones," J. Quality Assurance Hospitality Tourism, pp. 1-24, 2026. [10] Y. Ke, D. Liang, and K. Shang, "NeuroAlign: Dynamic Dual-Stream Alignment of Perception and Cognition for Zero-Shot Brain-Image Retrieval," in Proc. 2026 Int. Conf. Multimedia Retrieval, pp. 108-117, 2026. [11] A. F. Nia, "Using Advanced Machine Learning Techniques to Recognize Emotional States from a Hybrid fNIRS-EEG System," Ph.D. dissertation, Univ. Auckland, 2025. [12] U. Iqbal, "AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience," J. Eng. Comput. Intell. Rev., vol. 3, no. 1, pp. 114-138, 2025. [13] S. Shumba, "Multi-Modal Automated Major Depressive Disorder Detection," Ph.D. dissertation, Stellenbosch Univ., 2025. [14] M. Parvan, "ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics," arXiv:2605.01548, 2026. [15] E. G. Ahsaei, "Constructing and Analyzing Neural Network Dynamics for Information Objectives and Working Memory," Ph.D. dissertation, Washington Univ. St. Louis, 2021. [16] U. Iqbal, "AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries," J. Eng. Comput. Intell. Rev., vol. 3, no. 2, pp. 173-193, 2025. [17] P. M. Alamdari, "Multi-Persona Adaptive Recommendation via Cognitive Persona Transitions and Behavioral State Flow Modeling," 2025. [18] R. G. Praveen, P. Cardinal, and E. Granger, "Weakly supervised learning for facial affective behavior analysis: A review," IEEE Trans. Affect. Comput., 2026. [19] U. Imtiaz, F. Amin, and A. Khan, "Crash2Compromise: CrashGuard-FS for Security-Aware Linux File Updates," Multidiscip. Res. Comput. Inf. Syst., vol. 6, no. 2, pp. 294-310, 2026. [20] A. Khan, U. Imtiaz, and F. Amin, "Big Data Analytics in Advanced Cybersecurity: A US Study of Proactive Strategies and Innovative Solutions," Spanish J. Innov. Integrity, vol. 54, pp. 163-180, 2026. [21] S. Hussain, P. K. Jamwal, and P. Van Vliet, "Design synthesis and optimization of a 4-SPS intrinsically compliant parallel wrist rehabilitation robotic orthosis," J. Comput. Des. Eng., vol. 8, no. 6, pp. 1562-1575, 2021. [22] F. Jiang, N. Du, M. Yu, and Q. He, "EmoDNCL+: Dual-stream negative-sample-free contrastive learning with neurophysiological augmentation for EEG emotion recognition," J. King Saud Univ. Comput. Inf. Sci., vol. 38, no. 2, p. 30, 2026. [23] T. Li, Y. Yan, F. Dou, W. Song, and X. Zhang, "Cross-subject generalization for EEG decoding: a survey of deep learning methods," Prog. Biomed. Eng., vol. 8, no. 2, p. 022013, 2026. [24] D. Bailey, "Why Adaptive AI Has No Inner Life: A Structural Account of Function Without Phenomenology," 2026. [25] J. Duan et al., "Critical changes in whole-brain gene networks in response to small-cell lung cancer as revealed by single-nucleus RNA sequencing," Frontiers Immunol., vol. 17, p. 1860628, 2026. [26] U. Iqbal and Y. Bhutto, "Digital transformation through artificial intelligence and advance business analytic in American operational management," J. Theor. Appl. Econometrics, vol. 3, no. 1, pp. 37-50, 2026. [27] U. Iqbal, "AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics," J. Eng. Comput. Intell. Rev., vol. 2, no. 2, pp. 112-129, 2024. [28] U. Iqbal, S. Bekmez, and F. A. Qurashi, "Operational Risk Management Through Machine Learning and Business Intelligence in U.S. Businesses," Spanish J. Innov. Integrity, vol. 54, pp. 239-253, 2026.