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Non-Invasive Brain-computer Interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction

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SN Comprehensive Clinical Medicine 2023年第1期5卷 1-12页

作者： Zhan, Gege Su, Haolong Wang, Pengchao Wang, Junkongshuai Jiang, Haifeng Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Yiwu City China

Methamphetamine addiction is a brain disease that causes abnormalities in the structure and function of the brain. EEG, a common signal acquired based on the noninvasive brain-computer interface, can reflect the altered brain activity associated with methamphetamine addiction. EEG-based analysis methods provide a perspective to explore the neural mechanisms of methamphetamine addiction and the effects on brain activity. This paper reports the results of a review of EEG-based assessment and classification of methamphetamine addiction. Current methods commonly used in EEG-based methamphetamine addiction research include traditional resting-state EEG analysis, brain network analysis, and analysis of event-related potentials. A small number of studies have classified methamphetamine addiction and healthy individuals based on resting state EEG features or event-related potentials. EEG is one of the common tools used to examine the effects of methamphetamine on brain function. In follow-up studies, new methods for analyzing resting-state EEG and event-related potentials may help to investigate the neural mechanisms of methamphetamine addiction.

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on Electronic Information engineering and computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Using Determinant Point Process in Generative Adversarial Networks for SSVEP Signals Synthesis

Using Determinant Point Process in Generative Adversarial Ne...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Junkongshuai Wang Lu Wang Jiaguan Han Wei Mu Pengchao Wang Xueze Zhang Gege Zhan Lihua Zhang Zhongxue Gan Xiaoyang Kang MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics Fudan University Shanghai China Ji Hua Laboratory Foshan China Yiwu Research Institute of Fudan University Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Steady-state visual evoked potential (SSVEP) is one of the main paradigms of brain-computer interface (BCI). However, the acquisition method of SSVEP can cause subject fatigue and discomfort, leading to the insufficiency of SSVEP databases. Inspired by generative determinantal point process (GDPP), we utilize the determinantal point process in generative adversarial network (GAN) to generate SSVEP signals. We investigate the ability of the method to synthesize signals from the Benchmark dataset. We further use some evaluation metrics to verify its validity. Results prove that the usage of this method significantly improved the authenticity of generated data and the accuracy (97.636%) of classification using deep learning in SSVEP data augmentation.

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Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone 28th

Ghost Swarms: Learning Swarm Rules from Environmental Chang...

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28th European Conference on Genetic Programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Alharthi, Khulud Abdallah, Zahraa S. Hauert, Sabine Bristol Robotics Laboratory University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia

ISBN: (纸本)9783031899904

Swarm behaviours emerge from agents interacting with their local environment following simple rules. While directly observing each agent can be challenging, their collective behaviour leaves detectable environmental imprints that could offer insights into the underlying swarm dynamics. However, this task is complex due to the hidden and interconnected relationships between the rules governing agent interactions, the emergent swarm behaviour, and the environmental changes generated by this behaviour. In this work, we propose a method for extracting human-readable controllers from demonstrations showing only observable environmental imprints caused by the swarm. This approach explores whether these environmental imprints can reveal the swarm’s actions, even when the individual agents are challenging to track. Our approach eliminates the need for prior knowledge about the controller or its structure, enabling the successful learning of controllers from a single demonstration. We provide a novel method for understanding and managing both natural and engineered swarms by utilising the environmental imprints left by swarm behaviours, even when direct observation of the swarm’s actions is not feasible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental Imprints Imitation Learning Swarm Behaviour

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Convolutional Neural Network and Adversarial Autoencoder in EEG images classification 5

Convolutional Neural Network and Adversarial Autoencoder in ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Nasybullin, Albert Kurkin, Semen Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781665442824

In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities. © 2021 IEEE

关键词： computer vision

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers

arXiv

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arXiv 2024年

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian The Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States The Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States The Brady Urological Institute Johns Hopkins University BaltimoreMD United States The Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States The Department of Computer Science The Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024, CC BY-NC-ND.

关键词： Controllers

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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ThiNet Based Pruning Method for GAN Based Steganography Framework UT-GAN 15

ThiNet Based Pruning Method for GAN Based Steganography Fram...

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15th International Symposium on Signals, Circuits and Systems, ISSCS 2021

作者： Li, Qifen Li, Sili Tan, Shunquan Li, Bin Shenzhen University College of Computer Science and Software Engineering Shenzhen Key Laboratory of Media Security Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China

ISBN: (纸本)9781665449427

With the application of deep-learning framework, both the steganography and steganalysis gain superior performance than before, which has been testified by the previous researchers through experiments. Though the deep-learning models bring the excellent performance, they also result in the over-parameterized models and redundancy in computation and memory storage which make it hard for them to deploy. In this paper, we propose a ThiNet based scheme to prune the state-of the-Art steganographic model based on GAN-UTGAN. We evaluate and sort the significance of the channels of a layer according to the output of the next layer by using the greedy algorithm and obtain the channels which have the similar impact on the next layer compared with all the channels before pruning. The experimental results conducted on SZUBase, BOSSBase, and BOWS2 dataset demonstrate that our proposed ThiNet based method could compact the original UT-GAN model effectively with about only two percent of parameters of the original model and have about only a little decline in detection error rate when analyzed by SRM with ensemble classifier, which has outperformed the ASDL-GAN method. © 2021 IEEE.

关键词： Steganography

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Multiple Autoencoder-Based Anomaly Detection with Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Tan, Xiaofeng Gao, Can Zhou, Jie Yue, Xiaodong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

Anomaly detection is a practical and essential research topic with a wide range of applications. However, existing anomaly detection methods may face challenges when handling high-dimensional data with complex distributions. In this study, we propose a multiple autoencoder-based anomaly detection method with the aid of fuzzy rough sets. Specifically, the autoencoder is first improved by introducing the kernel fuzzy relation to enhance its representation capability in low-dimensional space. Then, the theory of fuzzy rough sets is employed to perform anomaly detection in the learned low-dimensional representation by fusing multi-view proximity-based information. Finally, to handle complex data, multiple autoencoders are utilized to collaboratively detect anomalies by integrating local anomaly information from different perspectives. Comparative experiments conducted on the selected datasets reveal that the proposed method is superior to state-of-the-art methods, improving over classical autoencoder by 5.58% in terms of the AUC-ROC index. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

arXiv

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arXiv 2023年

作者： Tang, Chao Huang, Dehao Ge, Wenqi Liu, Weiyu Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://***/view/graspgpt. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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