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Haifeng Li

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5 papers

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AAAI Conference 2026 Conference Paper

Leveraging Visual Blur Perception Characteristics for EEG Decoding

Wenchao Liu
Hongwei Li
Zhouyang Xu
Lin Ma
Haifeng Li

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.

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NeurIPS Conference 2024 Conference Paper

EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals

Guagnyu Wang
Wenchao Liu
Yuhong He
Cong Xu
Lin Ma
Haifeng Li

Electroencephalography (EEG) is crucial for recording brain activity, with applications in medicine, neuroscience, and brain-computer interfaces (BCI). However, challenges such as low signal-to-noise ratio (SNR), high inter-subject variability, and channel mismatch complicate the extraction of robust, universal EEG representations. We propose EEGPT, a novel 10-million-parameter pretrained transformer model designed for universal EEG feature extraction. In EEGPT, a mask-based dual self-supervised learning method for efficient feature extraction is designed. Compared to other mask-based self-supervised learning methods, EEGPT introduces spatio-temporal representation alignment. This involves constructing a self-supervised task based on EEG representations that possess high SNR and rich semantic information, rather than on raw signals. Consequently, this approach mitigates the issue of poor feature quality typically extracted from low SNR signals. Additionally, EEGPT's hierarchical structure processes spatial and temporal information separately, reducing computational complexity while increasing flexibility and adaptability for BCI applications. By training on a large mixed multi-task EEG dataset, we fully exploit EEGPT's capabilities. The experiment validates the efficacy and scalability of EEGPT, achieving state-of-the-art performance on a range of downstream tasks with linear-probing. Our research advances EEG representation learning, offering innovative solutions for bio-signal processing and AI applications. The code for this paper is available at: https: //github. com/BINE022/EEGPT

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YNIMG Journal 2022 Journal Article

Growth charts of brain morphometry for preschool children

Hongxi Zhang
Jia Li
Xiaoli Su
Yang Hu
Tianmei Liu
Shaoqing Ni
Haifeng Li
Xi-Nian Zuo

Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).

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AAAI Conference 2005 Conference Paper

The Regularized EM Algorithm

Haifeng Li

The EM algorithm heavily relies on the interpretation of observations as incomplete data but it does not have any control on the uncertainty of missing data. To effectively reduce the uncertainty of missing data, we present a regularized EM algorithm that penalizes the likelihood with the mutual information between the missing data and the incomplete data (or the conditional entropy of the missing data given the observations). The proposed method maintains the advantage of the conventional EM algorithm, such as reliable global convergence, low cost per iteration, economy of storage, and ease of programming. We also apply the regularized EM algorithm to fit the finite mixture model. Our theoretical analysis and experiments show that the new method can efficiently fit the models and effectively simplify over-complicated models.

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NeurIPS Conference 2003 Conference Paper

Efficient and Robust Feature Extraction by Maximum Margin Criterion

Haifeng Li
Tao Jiang
Keshu Zhang

A new feature extraction criterion, maximum margin criterion (MMC), is proposed in this paper. This new criterion is general in the sense that, when combined with a suitable constraint, it can actually give rise to the most popular feature extractor in the literature, linear discriminate analysis (LDA). We derive a new feature extractor based on MMC using a different constraint that does not depend on the nonsingularity of the within-class scatter matrix Sw. Such a dependence is a major drawback of LDA especially when the sample size is small. The kernelized (nonlin- ear) counterpart of this linear feature extractor is also established in this paper. Our preliminary experimental results on face images demonstrate that the new feature extractors are efﬁcient and stable.

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