Xiaopei Wu

IROS Conference 2025 Conference Paper

Towards Label-Free 3D Visual Grounding with Vision Foundation Models

• Xiaopei Wu
• Yuenan Hou
• Binbin Lin
• Xinge Zhu
• Yuexin Ma
• Haifeng Liu 0001
• Deng Cai 0001
• Xiao Sun

JBHI Journal 2024 Journal Article

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

• Shiang Hu
• Zhihao Zhang
• Xiaochu Zhang
• Xiaopei Wu
• Pedro A. Valdes-Sosa

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i. e. , aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, $\xi$ - $\pi$ was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. $\xi$ - $\pi$ was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that $\xi$ - $\pi$ improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that $\xi$ - $\pi$ produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that $\xi$ - $\pi$ approached the clinical findings in peak discovery. Overall, $\xi$ - $\pi$ offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. $\xi$ - $\pi$ is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

AAAI Conference 2024 Conference Paper

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

• Xiaopei Wu
• Liang Peng
• Liang Xie
• Yuenan Hou
• Binbin Lin
• Xiaoshui Huang
• Haifeng Liu
• Deng Cai

Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to generate pseudo labels for a student, and the quality of the pseudo labels is essential for the final performance. In this paper, we propose PatchTeacher, which focuses on partial scene 3D object detection to provide high-quality pseudo labels for the student. Specifically, we divide a complete scene into a series of patches and feed them to our PatchTeacher sequentially. PatchTeacher leverages the low memory consumption advantage of partial scene detection to process point clouds with a high-resolution voxelization, which can minimize the information loss of quantization and extract more fine-grained features. However, it is non-trivial to train a detector on fractions of the scene. Therefore, we introduce three key techniques, i.e., Patch Normalizer, Quadrant Align, and Fovea Selection, to improve the performance of PatchTeacher. Moreover, we devise PillarMix, a strong data augmentation strategy that mixes truncated pillars from different LiDAR scans to generate diverse training samples and thus help the model learn more general representation. Extensive experiments conducted on Waymo and ONCE datasets verify the effectiveness and superiority of our method and we achieve new state-of-the-art results, surpassing existing methods by a large margin. Codes are available at https://github.com/LittlePey/PTPM.

JBHI Journal 2020 Journal Article

To Explore the Potentials of Independent Component Analysis in Brain-Computer Interface of Motor Imagery

• Xiaopei Wu
• Bangyan Zhou
• Zhao Lv
• Chao Zhang

This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.