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Haofei Wang

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3 papers

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YNIMG Journal 2025 Journal Article

A dynamic spatiotemporal representation framework for deciphering personal brain function

Xuyang Wang
Ting Zou
Haofei Wang
Honghao Han
Huafu Chen
Vince D. Calhoun
Rong Li

Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into 'state sets' at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson's functional connectivity, the state-based representations serve as more discriminative 'brain fingerprints' for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.

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JBHI Journal 2024 Journal Article

cbPPGGAN: A Generic Enhancement Framework for Unpaired Pulse Waveforms in Camera-Based Photoplethysmography

Ze Yang
Haofei Wang
Bo Liu
Feng Lu

Camera-based photoplethysmography (cbP PG) is a non-contact technique that measures cardiac-related blood volume alterations in skin surface vessels through the analysis of facial videos. While traditional approaches can estimate heart rate (HR) under different illuminations, their accuracy can be affected by motion artifacts, leading to poor waveform fidelity and hindering further analysis of heart rate variability (HRV); deep learning-based approaches reconstruct high-quality pulse waveform, yet their performance significantly degrades under illumination variations. In this work, we aim to leverage the strength of these two methods and propose a framework that possesses favorable generalization capabilities while maintaining waveform fidelity. For this purpose, we propose the cbPPGGAN, an enhancement framework for cbPPG that enables the flexible incorporation of both unpaired and paired data sources in the training process. Based on the waveforms extracted by traditional approaches, the cbPPGGAN reconstructs high-quality waveforms that enable accurate HR estimation and HRV analysis. In addition, to address the lack of paired training data problems in real-world applications, we propose a cycle consistency loss that guarantees the time-frequency consistency before/after mapping. The method enhances the waveform quality of traditional POS approaches in different illumination tests (BH-rPPG) and cross-datasets (UBFC-rPPG) with mean absolute error (MAE) values of 1. 34 bpm and 1. 65 bpm, and average beat-to-beat (AVBB) values of 27. 46 ms and 45. 28 ms, respectively. Experimental results demonstrate that the cbPPGGAN enhances cbPPG signal quality and outperforms the state-of-the-art approaches in HR estimation and HRV analysis. The proposed framework opens a new pathway toward accurate HR estimation in an unconstrained environment.

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AAAI Conference 2023 Conference Paper

Learning a Generalized Gaze Estimator from Gaze-Consistent Feature

Mingjie Xu
Haofei Wang
Feng Lu

Gaze estimator computes the gaze direction based on face images. Most existing gaze estimation methods perform well under within-dataset settings, but can not generalize to unseen domains. In particular, the ground-truth labels in unseen domain are often unavailable. In this paper, we propose a new domain generalization method based on gaze-consistent features. Our idea is to consider the gaze-irrelevant factors as unfavorable interference and disturb the training data against them, so that the model cannot fit to these gaze-irrelevant factors, instead, only fits to the gaze-consistent features. To this end, we first disturb the training data via adversarial attack or data augmentation based on the gaze-irrelevant factors, i.e., identity, expression, illumination and tone. Then we extract the gaze-consistent features by aligning the gaze features from disturbed data with non-disturbed gaze features. Experimental results show that our proposed method achieves state-of-the-art performance on gaze domain generalization task. Furthermore, our proposed method also improves domain adaption performance on gaze estimation. Our work provides new insight on gaze domain generalization task.

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