In recent years, electroencephalography (EEG)-based visual decoding research has become a key direction for revealing brain processing mechanisms and realizing brain-computer interfaces. This emerging field has attracted extensive attention in the fields of brain science, cognitive neuroscience, and artificial intelligence. Among various approaches, contrastive learning has demonstrated strong performance in aligning multi-modal data, effectively enabling unified representations across modalities. However, during human visual perception, images are often subject to varying degrees of blurring due to the uneven distribution of retinal photoreceptor cells and the limited speed of lens accommodation. To address the mismatch between EEG and visual representations, we propose a novel visual decoding framework inspired by human perceptual blurring. Specifically, multi-level Gaussian blurring is applied to the visual stimuli to simulate human visual characteristics, followed by a feature selection module to construct robust visual representations. For EEG decoding, we design a lightweight and efficient network employing positively constrained spatial convolutions to identify channels associated with visual processing. The EEG and visual features are then aligned using contrastive learning. We evaluate the proposed framework on the Things-EEG dataset. Experimental results show significant improvements in the zero-shot brain-to-image retrieval task, achieving a top-1 accuracy of 80% and a top-5 accuracy of 96.9%, surpassing previous state-of-the-art methods by margins of 29.1% and 17.2%, respectively. These findings highlight the potential of incorporating perceptual properties into EEG-based visual decoding.

Empirical mode decomposition (EMD) is a well-established technique to decompose nonstationary signals into intrinsic oscillatory modes, which is essential to catch crucial information hidden in nonstationary signals. However, when decomposing signals with intermittency or closely spaced frequency modes, EMD suffers from severe mode mixing problems (MMP). Therefore, we develop a novel adaptively optimized masking EMD (AOMEMD), a generalized, automated method to solve MMP. The work focuses on two aspects: i) Masking signals with different scales are adaptively iteratively generated based on Hilbert analytic solution and applied to raw data to assist EMD in solving MMP. ii) A universal mode mixing measure index (UMI) is proposed to quantitatively evaluate the extent of mode mixing. An optimization routine is then developed to optimize the amplitude and frequency of masking signals with the objective of minimizing the UMI to further improve the frequency separation performance of AOMEMD. Experiments on synthetic and real-world signals demonstrate that AOMEMD outperforms EMD and its variants in effectively addressing MMP, yielding more reasonable and reliable results. AOMEMD can separate modes with frequency ratios up to 0.9.