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pro vyhledávání: '"Visual decoding"'
Autor:
Guo, Zhanqiang, Wu, Jiamin, Song, Yonghao, Bu, Jiahui, Mai, Weijian, Zheng, Qihao, Ouyang, Wanli, Song, Chunfeng
Human's perception of the visual world is shaped by the stereo processing of 3D information. Understanding how the brain perceives and processes 3D visual stimuli in the real world has been a longstanding endeavor in neuroscience. Towards this goal,
Externí odkaz:
http://arxiv.org/abs/2411.12248
Autor:
Li, Yueyang, Kang, Zijian, Gong, Shengyu, Dong, Wenhao, Zeng, Weiming, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI) and has transformative potential for neural sensory rehabilitation. While multimodal contrastive repre
Externí odkaz:
http://arxiv.org/abs/2412.17337
Decoding visual stimuli from neural recordings is a critical challenge in the development of brain-computer interfaces (BCIs). Although recent EEG-based decoding approaches have made progress in tasks such as visual classification, retrieval, and rec
Externí odkaz:
http://arxiv.org/abs/2410.23754
Autor:
Choi, Minsuk, Ishikawa, Hiroshi
Decoding neural representations of visual stimuli from electroencephalography (EEG) offers valuable insights into brain activity and cognition. Recent advancements in deep learning have significantly enhanced the field of visual decoding of EEG, prim
Externí odkaz:
http://arxiv.org/abs/2409.05279
Autor:
Bao, Guangyin, Miao, Duoqian
Exploring the mysteries of the human brain is a long-term research topic in neuroscience. With the help of deep learning, decoding visual information from human brain activity fMRI has achieved promising performance. However, these decoding models re
Externí odkaz:
http://arxiv.org/abs/2409.02044
Autor:
Zhao, Long, Woo, Sanghyun, Wan, Ziyu, Li, Yandong, Zhang, Han, Gong, Boqing, Adam, Hartwig, Jia, Xuhui, Liu, Ting
In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data, it reduces redundancy and emphasizes key features for high-quality ge
Externí odkaz:
http://arxiv.org/abs/2410.04081
Autor:
Bao, Guangyin, Zhang, Qi, Gong, Zixuan, Zhou, Jialei, Fan, Wei, Yi, Kun, Naseem, Usman, Hu, Liang, Miao, Duoqian
Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across individuals has prompted the development of deep learning models tailo
Externí odkaz:
http://arxiv.org/abs/2404.13282
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-quality images in cross-subject tasks is a challenging problem due to pr
Externí odkaz:
http://arxiv.org/abs/2404.12630
How to decode human vision through neural signals has attracted a long-standing interest in neuroscience and machine learning. Modern contrastive learning and generative models improved the performance of visual decoding and reconstruction based on f
Externí odkaz:
http://arxiv.org/abs/2403.07721
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