Author name cluster

Lu Cao

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7 papers

2 author rows

ICLR Conference 2025 Conference Paper

Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings

Di Wu 0057
Siyuan Li 0002
Chen Feng
Lu Cao
Yue Zhang
Jie Yang 0033
Mohamad Sawan

Recent advancements in brain-computer interfaces (BCIs) and deep learning have made decoding lexical tones from intracranial recordings possible, providing the potential to restore the communication ability of speech-impaired tonal language speakers. However, data heterogeneity induced by both physiological and instrumental factors poses a significant challenge for unified invasive brain tone decoding. Particularly, the existing heterogeneous decoding paradigm (training subject-specific models with individual data) suffers from the intrinsic limitation that fails to learn generalized neural representations and leverages data across subjects. To this end, we introduce Homogeneity-Heterogeneity Disentangled Learning for Neural Representations (H2DiLR), a framework that disentangles and learns the homogeneity and heterogeneity from intracranial recordings of multiple subjects. To verify the effectiveness of H2DiLR, we collected stereoelectroencephalography (sEEG) from multiple participants reading Mandarin materials containing 407 syllables (covering nearly all Mandarin characters). Extensive experiments demonstrate that H2DiLR, as a unified decoding paradigm, outperforms the naive heterogeneous decoding paradigm by a large margin. We also empirically show that H2DiLR indeed captures homogeneity and heterogeneity during neural representation learning.

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AAAI Conference 2021 Conference Paper

Brain Decoding Using fNIRS

Lu Cao
Dandan Huang
Yue Zhang
Xiaowei Jiang
Yanan Chen

Brain activation can reflect semantic information elicited by natural words and concepts. Increasing research has been conducted on decoding such neural activation patterns using representational semantic models. However, prior work decoding semantic meaning from neurophysiological responses has been largely limited to ECoG, fMRI, MEG, and EEG techniques, each having its own advantages and limitations. More recently, the functional near infrared spectroscopy (fNIRS) has emerged as an alternative hemodynamic-based approach and possesses a number of strengths. We investigate brain decoding tasks under the help of fNIRS and empirically compare fNIRS with fMRI. Primarily, we find that: 1) like fMRI scans, activation patterns recorded from fNIRS encode rich information for discriminating concepts, but show limits on the possibility of decoding fine-grained semantic clues; 2) fNIRS decoding shows robustness across different brain regions, semantic categories and even subjects; 3) fNIRS has higher accuracy being decoded based on multi-channel patterns as compared to single-channel ones, which is in line with our intuition of the working mechanism of human brain. Our findings prove that fNIRS has the potential to promote a deep integration of NLP and cognitive neuroscience from the perspective of language understanding. We release the largest fNIRS dataset by far to facilitate future research.

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IJCAI Conference 2021 Conference Paper

When Computational Representation Meets Neuroscience: A Survey on Brain Encoding and Decoding

Lu Cao
Dandan Huang
Yue Zhang

Real human language mechanisms and the artificial intelligent language processing methods are two independent systems. Exploring the relationship between the two can help develop human-like language models and is also beneficial to reveal the neuroscience of the reading brain. The flourishing research in this interdisciplinal research field calls for surveys to systemically study and analyze the recent successes. However, such a comprehensive review still cannot be found, which motivates our work. This article first briefly introduces the interdisciplinal research progress, then systematically discusses the task of brain decoding from the perspective of simple concepts and complete sentences, and also describes main limitations in this field and put forward with possible solutions. Finally, we conclude this survey with certain open research questions that will stimulate further studies.

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YNICL Journal 2019 Journal Article

Changes in default mode network connectivity in different glucose metabolism status and diabetes duration

Huanghui Liu
Jun Liu
Limin Peng
Zhichao Feng
Lu Cao
Huasheng Liu
Hui Shen
Dewen Hu

AIMS/HYPOTHESES: It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to investigate the neural basis of cognitive dysfunction in type 2 diabetes (T2D) patients. Alterations in brain functional connectivity may underlie diabetes-related cognitive dysfunction and brain damage. The aim of this study was to investigate the changes in default mode network (DMN) connectivity in different glucose metabolism status and diabetes duration. METHODS: We used a seed-based fMRI analysis to investigate positive and negative DMN connectivity in four groups (39 subjects with normal glucose metabolism [NGM], 23 subjects with impaired glucose metabolism [IGM; i.e., prediabetes], 59 T2D patients with a diabetes duration of <10 years, and 24 T2D patients with a diabetes duration of ≥10 years). RESULTS: Negative DMN connectivity increased and then regressed with deteriorating glucose metabolism status and extending diabetes duration. DMN connectivity showed a significant correlation with diabetes duration. CONCLUSION/INTERPRETATION: This study suggests that DMN connectivity may exhibit distinct patterns in different glucose metabolism status and diabetes duration, providing some potential neuroimaging evidence for early diagnosis and further understanding of the pathophysiological mechanisms of diabetic brain damage.

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ICRA Conference 2019 Conference Paper

Customized Object Recognition and Segmentation by One Shot Learning with Human Robot Interaction

Ping Guo
Lidan Zhang
Lu Cao
Yingzhe Shen
Xuesong Shi
Haibing Ren
Yimin Zhang 0002

There are two difficulties to utilize state-of-the-art object recognition/detection/segmentation methods to robotic applications. First, most of the deep learning models heavily depend on large amounts of labeled training data, which are expensive to obtain for each individual application. Second, the object categories must be pre-defined in the dataset, thus not practical to scenarios with varying object categories. To alleviate the reliance on pre-defined big data, this paper proposes a customized object recognition and segmentation method. It aims to recognize and segment any object defined by the user, given only one annotation. There are three steps in the proposed method. First, the user takes an exemplar video of the target object with the robot, defines its name, and mask its boundary on only one frame. Then the robot automatically propagates the annotation through the exemplar video based on a proposed data generation method. In the meantime, a segmentation model continuously updates itself on the generated data. Finally, only a lightweight segmentation net is required at testing stage, to recognize and segment the user-defined object in any scenes.

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IROS Conference 2018 Conference Paper

HERO: Accelerating Autonomous Robotic Tasks with FPGA

Xuesong Shi
Lu Cao
Dawei Wang
Ling Liu
Ganmei You
Shuang Liu
Chunjie Wang

The Heterogeneous Extensible Robot Open (HERO) platform is designed for autonomous robotic research. While bringing in the flexible computational capacities by CPU and FPGA, it addresses the challenges of heterogeneous computing by embracing OpenCL programming. We propose heterogeneous computing approaches for three fundamental robotic tasks: simultaneous localization and mapping (SLAM), motion planning and convolutional neural network (CNN) inference. With FPGA acceleration, the SLAM and motion planning tasks are performed 2–4 times faster on the HERO platform against fine-tuned software implementation. For CNN inference, it can process 20–30 images per second with the network of VGG-16 or ResNet-50. We expect the open platform and the developing experiences shared in this paper can facilitate future robotic research, especially for those compute intensive tasks of perception, movement and manipulation.

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IROS Conference 2010 Conference Paper

Spatial resolution for robot to detect objects

Lu Cao
Yoshinori Kobayashi
Yoshinori Kuno

In this paper, we report on our development of a robotic system that assists people in accomplishing simple tasks in daily life (e. g. , retrieving objects for handicapped and elderly people). These tasks, inevitably involve detecting various kinds of objects. In particular, here, we present an interactive method to detect objects using spatial information. Our experimental results confirm the usefulness and efficiency of our system. We also show how the approach can be improved and highlight necessary directions for future research.

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