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2nd IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Sakazawa, Shigeyuki Myodo, Emi Tasaka, Kazuyuki Yanagihara, Hiromasa Osaka Inst Technol Fac Informat Sci & Technol Osaka Japan KDDI R&D Res Saitama Japan

ISBN: (纸本)9781728111988

Deep neural network (DNN) is a kind of intellectual property considering its usefulness and cost to develop. This paper proposes watermarking to a trained DNN models to protect its copyright. The proposed method has a remarkable feature for watermark detection process, which can decode the embedded pattern cumulatively and visually. In the experiment, we can embed a specific visual pattern using 5,000 or 60,000 images on the pretrained image classification DNN model. Then the embedded pattern is decoded using 20 images out of the 5,000 or 60,000 images, while a performance degradation to the original image classification task is small. At the conference site, real time and animated visual decoding demonstration is performed.

关键词： Deep neural network Digital watermark visual decoding

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Edge-assistant visual Objects decoding using Sparse Representation 7

Edge-assistant Visual Objects Decoding using Sparse Represen...

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7th International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Wang, Lijun Li, Zhonglin Wang, Linyuan Tong, Li Zeng, Ying Yan, Bin China Natl Digital Switching Syst Engn & Technol Zhengzhou Peoples R China

ISBN: (纸本)9781479958382

visual objects decoding with functional magnetic resonance imaging (fMRI) often merely depends on the brain activity, using pattern classification to decode information about visual stimuli from patterns of activity. However, the spatial resolution of fMRI is still limited to the > mm range. Limited by its spatial resolution, fMRI voxels lack high-spatial-frequency information of visual stimuli. The overcomplete sparse representation can effectively match the sparse coding strategy in the primary visual cortex of human. Thus, it provides a probable way to represent the high-spatial-frequency information. Aiming to improve classification performance of images which were visual stimuli presented to participants, this paper proposed an edge-assistant approach for visual stimuli decoding, using sparse representation to supplement the ensemble of early visual voxels with high-spatial-frequency information. We supplemented early visual voxels with the sparse representation coefficients of edge patches and significantly improved the classification performance in a four-category (car, face, building, and animal) object classification analysis, which has valuable reference for practical fMRI-based image retrieval system.

关键词： visual decoding fMRI dictionary learning sparse representation high-spatial-frequency information

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visual Detection and decoding Skills of Aerial Photography by Adults with Autism Spectrum Disorder (ASD)

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JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2022年第3期52卷 1346-1360页

作者： Marciano, Hadas Gal, Eynat Kimchi, Ruth Hedley, Darren Goldfarb, Yael Bonneh, Yoram S. Univ Haifa Inst Informat Proc & Decis Making IIPDM Ergon & Human Factors Unit 199 Aba Khoushy Ave IL-3498838 Haifa Israel Tel Hai Coll Stress & Resilience Res Ctr Qiryat Shemona Israel Univ Haifa Dept Occupat Therapy Haifa Israel Univ Haifa Dept Psychol Haifa Israel Univ Haifa Inst Informat Proc & Decis Making Haifa Israel La Trobe Univ Olga Tennison Autism Res Ctr Sch Psychol & Publ Hlth Melbourne Vic Australia Bar Ilan Univ Fac Life Sci Sch Optometry & Vis Sci Ramat Gan Israel

Despite challenges in social communication skills people with ASD often display strengths in visual processing. Aerial photography analysis is an occupation reliant on strong visual processing skills that matches this unique profile. We investigated basic-vision and "real-life" visual tasks in 20 cognitively-able young adults with ASD and 20 typically-developed (TD) "gamers". Basic-vision tests included visual-Search, Embedded-Figures, and Vigilance;"real-life" tests included aerial-photograph detection and identification. Groups performed equally well, and did not differ significantly on any tasks. The study demonstrates strong visual skills in people with ASD in basic and "real-life" settings, and supports the idea that they may be well suited for employment in occupations that demand high visual perception skills such as aerial photography analysis.

关键词： Autism spectrum disorders Cognition (attention learning memory) Vocational labor force participation visual search visual perception visual decoding

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From sight to insight: A multi-task approach with the visual language decoding model

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INFORMATION FUSION 2024年 112卷

作者： Huang, Wei Yang, Pengfei Tang, Ying Qin, Fan Li, Hengjiang Wu, Diwei Ren, Wei Wang, Sizhuo Li, Jingpeng Zhu, Yucheng Zhou, Bo Sun, Jingyuan Li, Qiang Cheng, Kaiwen Yan, Hongmei Chen, Huafu Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Ctr Mental Hlth Sch Life Sci & Technol Chengdu 611731 Peoples R China Univ Elect Sci & Technol China High Field Magnet Resonance Brain Imaging Key Lab MOE Key Lab Neuroinformat Chengdu 610054 Peoples R China Sichuan Int Studies Univ Coll Language Intelligence Chongqing 400031 Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Ctr Mental Hlth Sch Med Chengdu 610072 Peoples R China Katholieke Univ Leuven Dept Comp Sci B-3001 Leuven Belgium Georgia State Univ Georgia Inst Technol Triinst Ctr Translat Res Neuroimaging & Data Sci T Atlanta GA 30303 USA Emory Univ Atlanta GA 30303 USA

visual neural decoding aims to unlock the mysteries of how the human brain interprets the visual world through predicting perceived visual information from visual neural activity. While early studies made some progress in decoding visual activity for singular type of information, they failed to concurrently reveal the multi-level interweaving linguistic information in the brain. Here, we developed a novel visual Language decoding Model (VLDM) that can simultaneously decode the main categories, semantic labels, and textual descriptions of visual stimuli from visual activities. The large-scale NSD dataset was utilized to ensure the efficiency of the decoding model in joint training and evaluation across multiple tasks. For category decoding, we achieved the effective classification of 12 categories with an accuracy of nearly 70 %, significantly surpassing the chance level. For label decoding, we attained the precise prediction of 80 specific semantic labels with a 16-fold improvement over the chance level. For text decoding, the scores of the decoded text surpassed the corresponding baseline levels by remarkable margins on six evaluation metrics. These results highlight the complexity of how the brain processes visual information and the close connection between visual perception and language cognition. This study contributes significantly to extensive applications in multi-layered brain-computer interfaces, potentially leading to more natural and efficient human-computer interaction experiences.

关键词： visual decoding Multitask decoding Functional magnetic resonance imaging

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Neural decoding of visual Information Across Different Neural Recording Modalities and Approaches

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Machine Intelligence Research 2022年第5期19卷 350-365页

作者： Yi-Jun Zhang Zhao-Fei Yu Jian.K.Liu Tie-Jun Huang Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science Peking UniversityBeijing 100190China Institute for Artificial Intelligence Peking UniversityBeijing 100190China School of Computing University of LeedsLeeds LS29JTUK Beijing Academy of Artificial Intelligence Beijing 100190Chin

Vision plays a peculiar role in *** information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent *** advances have led to the development of brain-inspired algorithms and models for machine *** of the key components of these methods is the utilization of the computational principles underlying biological ***,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual ***,there is a high demand for mapping out functional models for reading out visual information from neural ***,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.

关键词： Neural decoding machine learning deep learning visual decoding brain-inspired vision

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decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds

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NEUROIMAGE 2014年第Part2期102卷 435-450页

作者： Kuo, Po-Chih Chen, Yong-Sheng Chen, Li-Fen Hsieh, Jen-Chuen Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Chiao Tung Univ Inst Biomed Engn Hsinchu Taiwan Natl Yang Ming Univ Inst Brain Sci Taipei 112 Taiwan Taipei Vet Gen Hosp Integrated Brain Res Unit Dept Med Res Taipei Taiwan

visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data. In the proposed decoding process, principal component analysis is applied to extract temporal principal components (TPCs) from the visual cortical activity estimated by a beamforming method. The spatial distribution of each TPC is in a high-dimensional space and can be mapped to the corresponding spatiotemporal component (STC) on a low-dimensional manifold. Once the linear mapping between the STC and the wavelet coefficients of the stimulus image is determined, the decoding process can synthesize an image resembling the stimulus image. The encoding process is performed by reversing the mapping or transformation in the decoding model and can predict the spatiotemporal brain activity from a stimulus image. In our experiments using visual stimuli containing eleven combinations of checkerboard patches, the information of spatial layout in the stimulus image was revealed in the embedded manifold. The correlation between the reconstructed and original images was 0.71 and the correlation map between the predicted and original brain activity was highly correlated to the map between the original brain activity for different stimuli (r = 0.89). These results suggest that the temporal component is important in visual processing and manifolds can well represent the information related to visual perception. (c) 2014 Published by Elsevier Inc.

关键词： visual decoding visual encoding Magnetoencephalography Manifold

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Cross-fixation interactions of orientations suggest high-to-low-level decoding in visual working memory

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VISION RESEARCH 2022年 190卷 107963-107963页

作者： Luu, Long Zhang, Mingsha Tsodyks, Misha Qian, Ning Columbia Univ Zuckerman Inst Dept Neurosci Dept Physiol & Cellular Biophys New York NY 10027 USA Beijing Normal Univ IDG McGovern Inst Brain Res State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Inst Adv Study Sch Nat Sci Simons Ctr Syst Biol Princeton NJ 08540 USA

Sensory encoding (how stimuli evoke sensory responses) is known to progress from low- to high-level features. decoding (how responses lead to perception) is less understood but is often assumed to follow the same hierarchy. Accordingly, orientation decoding must occur in low-level areas such as V1, without cross-fixation interactions. However, a study, Ding, Cueva, Tsodyks, and Qian (2017), provided evidence against the assumption and proposed that visual decoding may often follow a high-to-low-level hierarchy in working memory, where higher-to-lower-level constraints introduce interactions among lower-level features. If two orientations on opposite sides of the fixation are both task relevant and enter working memory, then they should interact with each other. We indeed found the predicted cross-fixation interactions (repulsion and correlation) between orientations. Control experiments and analyses ruled out alternative explanations such as reporting bias and adaptation across trials on the same side of the fixation. Moreover, we explained the data using a retrospective high-to-low-level Bayesian decoding framework.

关键词： visual decoding Perceptual bias Memory noise Retrospective Bayesian

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A study of decoding human brain activities from simultaneous data of EEG and fMRI using MVPA

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AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018年第3期41卷 633-645页

作者： Zafar, Raheel Kamel, Nidal Naufal, Mohamad Malik, Aamir Saeed Dass, Sarat C. Ahmad, Rana Fayyaz Abdullah, Jafri M. Reza, Faruque Natl Univ Modern Languages Dept Engn Islamabad Pakistan Univ Teknol PETRONAS CISIR Perak Malaysia Univ Sains Malaysia Ctr Neurosci Serv & Res Kota Baharu 16150 Kelantan Malaysia Univ Sains Malaysia Sch Med Sci Dept Neurosci Kota Baharu 16150 Kelantan Malaysia

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants;the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

关键词： EEG fMRI visual decoding SVM DWT

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Semantics-Guided Hierarchical Feature Encoding Generative Adversarial Network for visual Image Reconstruction From Brain Activity

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 1267-1283页

作者： Meng, Lu Yang, Chuanhao Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

The utilization of deep learning techniques for decoding visual perception images from brain activity recorded by functional magnetic resonance imaging (fMRI) has garnered considerable attention in recent research. However, reconstructed images from previous studies still suffer from low quality or unreliability. Moreover, the complexity inherent to fMRI data, characterized by high dimensionality and low signal-to-noise ratio, poses significant challenges in extracting meaningful visual information for perceptual reconstruction. In this regard, we proposes a novel neural decoding model, named the hierarchical semantic generative adversarial network (HS-GAN), inspired by the hierarchical encoding of the visual cortex and the homology theory of convolutional neural networks (CNNs), which is capable of reconstructing perceptual images from fMRI data by leveraging the hierarchical and semantic representations. The experimental results demonstrate that HS-GAN achieved the best performance on Horikawa2017 dataset (histogram similarity: 0.447, SSIM-Acc: 78.9%, Peceptual-Acc: 95.38%, AlexNet(2): 96.24% and AlexNet(5): 94.82%) over existing advanced methods, indicating improved naturalness and fidelity of the reconstructed image. The versatility of the HS-GAN was also highlighted, as it demonstrated promising generalization capabilities in reconstructing handwritten digits, achieving the highest SSIM (0.783 +/- 0.038), thus extending its application beyond training solely on natural images.

关键词： visual decoding image reconstruction generative adversarial network fMRI

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Mind-bridge: reconstructing visual images based on diffusion model from human brain activity

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 953-963页

作者： Liu, Qing Zhu, Hongqing Chen, Ning Huang, Bingcang Lu, Weiping Wang, Ying East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Gongli Hosp Shanghai Pudong New Area Dept Radiol Shanghai 200135 Peoples R China Gongli Hosp Shanghai Pudong New Area Shanghai Hlth Commiss Sino French Cooperat Cent Lab Key Lab Artificial Intelligence AI Based Managemen Shanghai 200135 Peoples R China

Human brain vision is mysterious and complex, and it interprets the world through the connection between the brain and the eyes. In recent years, several methods have relied on fMRI to successfully reconstruct visual images from human brain activity. However, these reconstruction methods focus more on the semantics of the reconstruction image and lack attention to the image structure and foreground targets. To alleviate this problem, we propose a diffusion model-based image reconstruction architecture (Mind-Bridge) that utilizes fMRI to reconstruct visual images from human brain activity. Specifically, we first develop a novel Depth Structure Variational AutoEncoder (DSVAE) to capture image structural information at the initial stage. To obtain more foreground target information, we further introduce Edge estimation through the edge detection operator. In addition, we utilize Contrastive Language Image Pre-training (CLIP) text and image encoders as image and text prompt conditions for visual reconstruction. Finally, our proposed Mind-Bridge utilizes the Versatile Diffusion (VD) to fuse different stages of image information for visual images reconstruction. Qualitative and quantitative analysis results on the challenging Natural Scene Dataset (NSD) show that our proposed Mind-Bridge is effective.

关键词： Image reconstruction Diffusion model Functional magnetic resonance imaging (fMRI) visual decoding Variational autoencoder

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